Data Is Everybody’s Business
Management on the Cutting Edge series
Abbie Lundberg, series editor
Published in cooperation with MIT Sloan Management Review
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Leading in the Digital World: How to Foster Creativity, Collaboration, and
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The Ends Game: How Smart Companies Stop Selling Products and Start
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Open Strategy: Mastering Disruption from Outside the C-Suite
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The Transformation Myth: Leading Your Organization through Uncertain
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Winning the Right Game: How to Disrupt, Defend, and Deliver in a
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The Digital Multinational: Navigating the New Normal in Global Business
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Work without Jobs: How to Reboot Your Organization’s Work Operating
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The Future of Competitive Strategy: Unleashing the Power of Data and
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Productive Tensions: How Every Leader Can Tackle Innovation’s Toughest
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Data Is Everybody’s Business: The Fundamentals of Data Monetization
Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens
Data Is Everybody’s Business
The Fundamentals of Data Monetization
Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens
The MIT Press
Cambridge, Massachusetts
London, England
© 2023 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form by any electronic or
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Library of Congress Cataloging-in-Publication Data
Names: Wixom, Barbara Haley, 1969– author. | Beath, Cynthia Mathis, 1944– author. | Owens,
Leslie (Leslie Ann), 1972– author.
Title: Data is everybody’s business : the fundamentals of data monetization / Barbara H. Wixom,
Cynthia M. Beath, and Leslie Owens.
Description: Cambridge, Massachusetts : The MIT Press, [2023] | Series: Management on the
cutting edge | Includes bibliographical references and index.
Identifiers: LCCN 2022055462 (print) | LCCN 2022055463 (ebook) | ISBN 9780262048217
(hardcover) | ISBN 9780262375351 (epub) | ISBN 9780262375344 (pdf)
Subjects: LCSH: Electronic data processing—Economic aspects. | Big data—Economic aspects. |
Data mining—Economic aspects.
Classification: LCC HF5548.2 .W59 2023 (print) | LCC HF5548.2 (ebook) | DDC 658/.05—
dc23/eng/20221121
LC record available at https://lccn.loc.gov/2022055462
LC ebook record available at https://lccn.loc.gov/2022055463
10 9 8 7 6 5 4 3 2 1
d_r0
Contents
Series Foreword
Foreword
Introduction: Data Is Everybody’s Business
1 Data Monetization
2 Data Monetization Capabilities
3 Improving with Data
4 Wrapping with Data
5 Selling Information Solutions
6 Creating a Data Democracy
7 Data Monetization Strategy
8 Monetizing Your Data
Appendix: The Capability Assessment Worksheet
Acknowledgments
Notes
Index
Series Foreword
The world does not lack for management ideas. Thousands of researchers,
practitioners, and other experts produce tens of thousands of articles, books,
papers, posts, and podcasts each year. But only a scant few promise to truly
move the needle on practice, and fewer still dare to reach into the future of
what management will become. It is this rare breed of idea—meaningful to
practice, grounded in evidence, and built for the future—that we seek to
present in this series.
Abbie Lundberg
Editor in chief
MIT Sloan Management Review
Foreword
When Professor Barbara Wixom (Barb) joined MIT Sloan Center for
Information Systems Research (MIT CISR) as a principal research scientist
to lead the data research stream, she advocated for an advisory board of
senior global data leaders to help her. That’s us. Barb needed data experts
from a wide variety of organizations to participate in studies, set research
priorities, and vet insights. Our charge was to help keep the centers data
research relevant, edgy, and applicable.
We share our passion for all things data via chat, while visiting the MIT
campus, in executive education sessions, and in virtual meetups. Through
these interactions, we have gotten to know and trust Barb, as well as her
MIT CISR collaborators, Cynthia and Leslie, and each other. The teamwork
and sharing tend to spark ideas and new directions. When one of us has
something particularly unique underway, the MIT CISR team investigates
what we are doing and helps us find the “aha!” moments. They write up our
efforts as a case study when our approach might help others succeed.
In Q1 of 2021, Barb kicked off a virtual conversation for the board called
“Inspiring Hearts and Minds.” She asked us to reflect: why is data different
today versus when we all started in the field? And because of that
difference, what should we be doing as data leaders? She hypothesized that
data leaders had to become much more evangelical; she thought we were
carrying too much of the responsibility for our organizations’ data assets.
And, to communicate persuasively, data leaders needed a simple, everyday
business language that a broad base of people could understand. We agreed.
We felt it too.
In our ensuing conversation, we agreed that data needs to become an
expected competence for the majority, an evolution that requires patience,
commitment, and ongoing investment in talent and new ways of working.
To build and keep momentum, we’ve found that you need the diverse
perspectives (especially regarding customer needs) of a wide range of
people, from different functions, disciplines, generations, and levels, who
are personally invested.
At this point, we don’t need to persuade people that data has value.
Instead, we need to help them contribute to the shift from tactical, local
efforts to building enterprise-wide capabilities. We’ve found that when
people see data as within their purview (not a responsibility of a few or an
IT department), they spot innovation opportunities more readily. As data
leaders, we focus on generating and sharing trusted data assets and leading
data literacy programs. Ideally, because of our efforts, most of our
colleagues will feel comfortable using data to improve work or products.
Many of us already apply MIT CISR research in our organizations and
can point to the benefits. Now, we welcome having a book that pulls the
research together in a way that appeals to all. We plan to distribute and
discuss this book across our organizations, coach others in the easy-to-use
frameworks, and inspire our people to participate in data monetization to
• build data assets,
• create novel, data-driven ways of working and customer
experiences, and
• learn about data and share knowledge about data with colleagues.
We hope to rally our colleagues across the enterprise—from our frontline
employees to our senior leaders and board members—to embrace the idea
that data is everybody’s business. This book is for everybody.
—MIT CISR Data Research Advisory Board Members
Introduction: Data Is Everybody’s Business
Everyone will say that data is extremely important to the business. However, beyond
that, people don’t know what to do next.
—Mihir Shah, Fidelity Investments
It’s common for leaders who want to create value from data to look for
inspiration within companies like Google. They might go on a road trip and
tour Google’s California offices. While there, they might encounter
advanced technology and brilliant data scientists developing proprietary
artificial intelligence–based products, such as a map that automatically
updates when a business is open.1 But what else is behind a company like
Google’s success? It’s several things. It’s the expectation that everybody is
a data practitioner, inventing new data-driven work practices and sharing
them with others. It’s an environment where data has been converted into
data assets that people can find, trust, and use to address unmet business
needs without having to create manual, bespoke processes and controls.
And it’s a firm-wide push to convert such data assets into revenues since,
after all, the mission of Alphabet Inc. (Google’s parent company) is to
“organize the world’s information and make it universally accessible and
useful.”2
Like newlyweds returning from a fantastic honeymoon, leaders from
traditional companies might return to their offices after a field trip to Silicon
Valley and feel overwhelmed by what’s ahead. Of course, few organizations
have as much data as Google. But all organizations, including yours, have
lots of data. It can be internal (e.g., accounting data) or external (e.g.,
purchased data about consumer credit risk or household preferences). It can
be structured (e.g., customer orders) or unstructured (e.g., tweets). It could
live in a spreadsheet, the cloud of a consultant, an email archive, a data
warehouse, or a data lake, to name just a few spots. Organizations today are
great at amassing data, causing a data deluge that grows with every
technology advancement in storage, processing, electronics, networking,
and telecommunications.
Yes, in most organizations, data is everywhere. Yet typically, the data is
tied to some context.3 The data is shaped and constrained by the processes
that create it and govern it. It is stuck in closed platforms, replicated in
multiple locations, incomplete, inaccurate, and poorly defined. As a result,
organizations focus a lot of managerial attention on liberating data from
silos and applying it to new specific uses such as calculating customer
churn or spotting supply chain breaks. Such efforts are complicated and
filled with friction, and each time a new chance to use the data emerges,
somebody must overcome the same hurdles. Leveraging data to meet an
unanticipated challenge or opportunity seems like a Herculean feat.
Companies like Google take a different approach with their data. They
decontextualize data and prepare data assets that can be accessed and reused
for innumerable purposes. Such data assets are accurate, complete, current,
standardized, searchable, and understandable—assets that everybody across
the company can easily incorporate into their value-creating initiatives. The
“data” in the title of this book refers to data assets. The book will describe
how organizations develop data assets so they can be exploited repeatedly.
The use of “everybody” in the title also has significance. Data is not just
for people with “data” in their job title. For organizations to develop data
assets and exploit them over and over, many more people need to be in the
mix. Just as an organization’s financial results are the responsibility of more
than finance and accounting staff, just as customer retention requires more
than sales, and just as talent management is not solely owned by human
resources (HR), data responsibilities need to be held well beyond the data
teams.
But why does the title say that data is everybody’s “business?” Because
your organization should be using data to make money and save money.
The financial inflows generated from your organization’s collective data
investments should be larger than your financial outflows. If your
organization does not actively manage the amount of money you generate
from your data—that is, the extent to which you monetize your data—you
will limit your financial inflows. Worst case, you will lose money. The
concept of data monetization is a fundamental business concept that will be
explained in chapter 1.
Clear, Memorable, and Integrated Frameworks
Leaders often find themselves trying to develop people’s “data savvy”
without much support. Unlike other management topics that are well
covered in business schools and executive education, the field of data is
relatively new, and standards and core curriculum are still emerging.4 The
Data Management Association, for example, published the first edition of
its data management body of knowledge in 2009.5 As a result, organizations
have had to develop their own data training materials, create their own
terminology, and engage in a lot of trial and error to get stuff right.
This book offers simple language and integrated concepts that can
quickly raise people’s data savvy. There are three key frameworks, shown
in figure 0.1. After reading this book, any reader should be able to pick up a
marker and draw the frameworks on a whiteboard as context for data
monetization conversations.
The first framework summarizes the five data monetization capabilities
organizations use to develop data assets and make data monetization fast
and successful: data management, data platform, data science, customer
understanding, and acceptable data use. The capabilities are depicted as a
fan because they are closely related and work together. Capabilities are the
expertise developed from mastering practices to decontextualize data:
divorcing it from a specific condition or context and turning it into reusable
data assets. This expertise may be embodied in people (as abilities or
proficiencies) or in other material resources (tools, routines, technology,
forms, policies, and so forth). Over time, data monetization practices build
the organization’s muscle in the five data monetization capabilities.
Figure 0.1
Three key data monetization frameworks
The second framework describes three kinds of initiatives in which
organizations invest that generate financial returns from their data assets:
improving work, wrapping products with data-fueled features and
experiences, and selling information solutions. Each approach uses data
differently and has an ideal owner, a set of risks to mitigate, and unique
outcomes. Recognizing what makes these three approaches different is
crucially important; trying to run a selling initiative as if it were an
improving initiative can be disastrous. It would be like fixing a kitchen
appliance with the tools, talent, and expertise needed for landscaping the
yard. Organizations that understand the distinct requirements of each data
monetization approach can make the right investments, set realistic
expectations, and generate optimal returns.
The third framework offers a way to think about organizational design
for data monetization. It describes five organizational connections between
domain experts and data experts. The term domain refers to an area of
subject matter that is valued in an organization, not including subject matter
related to data. (For example, accounting, marketing, nursing, teaching, and
law enforcement are common domains.) Organizations can’t expect people
to embrace and engage with data monetization on their own—think about
how busy people are simply trying to keep up with business as usual! To
encourage changes in people’s behaviors and (ideally) habits, organizations
must actively establish connections between domain and data experts so
that people share knowledge, learn, and, ultimately, change things. When
people across your organization know how to leverage data assets and data
monetization capabilities to innovate, and when they participate in and take
responsibility for data monetization initiatives, then you know your
organization’s connections are working.
Collectively, the three frameworks work together. They reinforce each
other, working like a flywheel to produce positive momentum that picks up
over time. To apply the frameworks, you can begin anywhere. As you better
understand and build data monetization capabilities, you enable more and
different kinds of initiatives and you engage more people across the
organization. As you become more proficient at deploying and generating
returns from data monetization initiatives, you satisfy and excite data
monetization investors, participants, and benefactors. And as you activate
connections between domain and data experts all over the organization, you
grow the population of stakeholders who will build and use data
monetization capabilities and participate in initiatives.
Who Should Read This Book?
This book is for . . . everybody! That is, everybody who works in an
organization. It has been designed to be relevant to audiences at all levels of
data expertise and to appeal to people in organizations large and small,
commercial and noncommercial, national and global. Yes, even
philanthropic and public organizations engage in data monetization. This
book can help you—and it can help you help others. It is as relevant to
leaders who manage their organization’s data monetization strategy as it is
to the people who bring data monetization principles to life.
This book is not a list of clever ways to monetize data, although the
examples in this book might spark some ideas for you. It bypasses the pros
and cons of different data vendors or data architectures. Instead, this book
will help you articulate your ideas and move them to fruition. You will learn
how to monetize data successfully by focusing on a few essential
frameworks.
About the Research
The authors of this book are affiliated with the MIT Center for Information
Systems Research (MIT CISR), a global nonprofit research center nested
within the MIT Sloan School of Management. Founded in 1974, MIT CISR
is committed to helping organizational leaders manage technology
(including data) successfully. The center produces relevant academic
research for leaders grappling with contemporary technology management
challenges.
The academic researchers at MIT CISR seek to identify and understand a
phenomenon to explain and predict outcomes. The research behind this
book has examined how organizations generate value from data from many
angles over several decades. The organizations studied range from ones as
large as Microsoft to some as small as thirty-person start-up AdJuggler, as
diverse as airlines, high-end retailers, and data aggregators, both
commercial and noncommercial, including government agencies and
nonprofits, located all over the world. The theoretical foundations of the
research focus on organizations and the people in them, not computer
science.
MIT CISR research often starts with exploratory qualitative studies—
case studies and field observations—to understand the problems
organizations have, what solutions work, and what solutions don’t. Many of
those case studies appear in this book. Qualitative studies are followed by
confirmatory quantitative analyses, using interview and survey data.
Readers will find the results of those studies in this book. A variety of
theories are used to develop the research insights. In some cases,
collaborators with deep expertise (from MIT and other universities around
the world) help formulate new ideas or extend current thinking; at other
times, concepts from the marketing and management literatures are
borrowed and reapplied.
The research behind this book was carried out alongside practitioners,
data practitioners in particular. In 2015, MIT CISR initiated a data research
advisory board of chief data officers and chief analytics officers from MIT
CISR member organizations. These one-hundred-plus practitioners not only
patiently complete extensive surveys and participate in interviews but also
prioritize topics to be studied, debate research findings, and test out
frameworks. Their voices can be heard throughout this book.
How This Book Will Unfold
The book opens (chapter 1) by defining data monetization and a few other
foundational concepts—such as the data-insight-action process, value
creation, and value realization—that will reappear throughout the book.
Next, chapter 2 describes five enterprise capabilities that organizations need
for data monetization to succeed as well as how organizations build these
capabilities. Then, three chapters (chapters 3, 4, and 5) give in-depth looks
at the three kinds of initiatives you can use to monetize data: improving,
wrapping, and selling. You will explore the critical success factors for each
data monetization approach and learn how to create and realize value using
each one. In chapter 6, you will learn how to engage more people in your
organization in the work of data monetization. This work includes creating
connections and incentivizing people to interact and reuse data assets.
Chapter 7 describes the importance of establishing a data monetization
strategy and presents four strategy archetypes representing four different
ways to monetize data. Finally, chapter 8 will encourage you to make it
your business to monetize data.
Each chapter starts with questions for you to ask yourself. Research
findings and definitions of key terms are presented along the way. The
chapters offer in-depth case studies that contextualize the purpose and
application of the frameworks. Finally, each chapter ends with a Time to
Reflect section to help you apply its lessons and concepts to your own
situation. Enjoy the read!
1
Data Monetization
If I cannot articulate the value of an initiative in a monetized way, it’s a wish list; it’s
nothing but a wish list.
—Jeevan Rebba, Otsuka Pharmaceutical Companies
Work has changed profoundly in the past few years. Managers are
introducing new work practices that help employees innovate, not just
punch the clock. For example, customer journey mapping initiatives help
people understand customer perspectives and improve customer
experiences; design thinking inspires people to solve problems creatively
and to enhance products in appealing ways; and test and learn processes
support people in taking small risks with small ideas that have the potential
to develop into something big.
These new ways of working have created an opening for individual
employees to contribute directly to organizational success. Regardless of
what team they represent or how senior their role is, employees today are
more attentive to how their work impacts the whole organization and how
changes to their work might pay off. At CarMax, for example, employees
throughout the company can link their work to one of CarMax’s missions:
either they are trying to sell more cars, or they are trying to buy more cars.1
As a result of these clear goals, creative people across the company can
unleash ideas on how changes to the tasks they perform could help achieve
the CarMax mission. A salesperson, for example, could hypothesize that an
improvement in how sales leads are identified would get more cars sold, run
a local experiment, and demonstrate that the idea worked.
The data assets available to employees at modern organizations like
CarMax play a significant role in these new ways of working. Data assets
provide a single source of truth; they draw on more data and novel data,
often sourced from social media, mobile devices, artificial intelligence (AI),
and the Internet of Things (IoT). People at these organizations use data
assets to measure, validate, inform, persuade, and prompt—in fiscally and
socially responsible ways. Data assets are built to be monetized.
This book features many examples of people in organizations actively
pursuing data monetization. In the last decade, Microsoft used data to move
its business model from being product based to cloud services based and
saw its stock price soar.2 Banco Bilbao Vizcaya Argentaria (BBVA), a
financial services company, used data to become a digital-first financial
services provider. As of 2021, it had won Forresters award for overall
mobile banking digital experience in Europe five years in a row.3 Finally,
PepsiCo used data to identify and serve granular market needs and
transformed its transactional retailer relationships into collaborative
partnerships.4 These three organizations and their data monetization
journeys are featured in detail in chapters 2, 3, and 4, respectively. So, what
is data monetization?
Data monetization is turning data into money. Money is a crucial
resource for all organizations, public and private. Organizations need
money from customers, donors, or citizens, and they need to handle that
money prudently. Organizations use data not only to create valuable
benefits—customer and employee satisfaction, brand capital, desired
product enhancements, streamlined processes, or citizen welfare—but also
to purposefully realize financial value—money—to improve their bottom
line.
Data monetization is the generation of financial returns from data
assets.
These days, different kinds of organizations pay attention to many
different “bottom lines.” What number tells the world that your
organization is sustainably efficient and effective? It might be your net cash
flow, your net income, your unrestricted net income (if you are a nonprofit),
or some other measure of efficiency and effectiveness. In this book, bottom
line means the difference between money in and money out.
There is a world of difference between creating valuable benefits and
turning those benefits into money. We will call the first value creation. By
that, we mean creating benefits that are desirable and have the potential to
reach the bottom line. These benefits are common goals in data initiatives:
more streamlined processes, smoother supply chains, employee satisfaction,
or products that customers find desirable. This book is mainly about how to
create value from data.
We will call the second value realization. By that, we mean transforming
the value created by these initiatives into money, or, simply, more money in
or less money out. Value realization brings data monetization home.
Realizing value from data is about converting value created—efficiency or
customer value—into money or getting money directly from data by selling
it. The ultimate goal of data monetization is bottom line improvement—
reducing costs or growing earnings. Staying focused on realizing value
increases the odds that your data investments will pay off and that the
organization will not leave money on the table. So, even though this book is
mainly about how to create value, the imperative to realize any value you
create should always be kept in mind.
Questions to ask yourself
How does your organization create value using data today? How well
does your organization report on the financial returns that result from
these value-creating efforts? Can you track how much money data
contributes to your organization’s bottom line?
Research to consider
High-performing organizations—in terms of profitability, revenue
growth, innovation, and agility—report that data monetization accounts
for 10 percent more of their overall revenues than it does at their low-
performing peers.5
Creating Value from Data
Over the past several decades, organizations have learned a lot about
creating value from data. But arguably, the most important lesson is that
creating value from data requires that a person or a system take some action
it otherwise would not have taken. Data needs to be used to change the way
something is done or to produce something new. Better processes and
products create new value, not the data itself. This understanding is core to
the data value-creation process, commonly referred to as data-insight-
action. During this process, data is used by people (or systems) to produce
an insight, the insight informs an action, and the action results in a valuable
outcome. As figure 1.1 illustrates, data, insight, and action must all happen
before value creation can occur: to grow valuable fruit, a fruit tree needs
adequate soil and nutrients, the right amount of sun, and careful watering. If
the data value-creation process breaks down or stalls—maybe there’s a
well-planted seed but no light or no water—then the associated investments
in that little plant are merely sunk costs. This idea that the complete data-
insight-action process precedes the creation of value is a fundamental data
monetization idea that you may have encountered at a data-related
conference, course, or event. It’s a central idea in this book.
The data value-creation process (a.k.a. the data-insight-action
process) occurs when people or systems use data to develop insights
that inform action, which generates value.
Figure 1.1
The data value-creation process (a.k.a. the data-insight-action process)
Realizing Value from Data
Creating value from data is necessary but not sufficient. The final step is
making sure that whatever value is created—something better, something
new—contributes to the organization’s bottom line. In other words, the
“value created” needs to be turned into money. This step is value
realization. Unless financial value is realized, data has not been monetized
and the organization is now more costly.
Value realization occurs when value that has been created from data is
turned into money.
Think about it. Fruit doesn’t pick itself; when fruit stays on the tree, it is
neither eaten nor sold. As figure 1.2 illustrates, the whole point of
cultivating a fruit tree is for someone or some people to enjoy its fruits.
Value can be realized in one step or two steps. In the one-step process,
data is exchanged for money in some form, and the money you receive for
the data you sell is real and ready to count. Imagine an information business
such as Nielsen selling consumer behavior data to a television network.
Nielsen sets a price for the data; the network pays it. The data’s value is
realized with the sales transaction.
Figure 1.2
Value realization
Value realization is a two-step process if it entails first creating
something that has inherent value and then, second, cashing in that value so
that it becomes financial value. The first step of creating value is the result
of a data monetization initiative, but the second step, value realization, may
require the involvement of multiple stakeholders. Step two could be the
responsibility of a senior manager or leader if it requires higher levels of
authority. For example, say a recruiting team uses data to make the new hire
process more efficient. That’s step one, and the efficiencies are inherently
valuable. The crucial second step occurs when the efficiency gets turned
into money by, say, cutting headcount and reducing the budget for
onboarding new hires. When organizations have more resources than they
need to sustain routine operations, that’s referred to as “slack.” Thus, in the
second step, the slack created by the new, more efficient process is
removed, making the organization better off financially.
Maybe a different data monetization initiative delivers a product
enhancement that customers value. In that case, the second step requires
that the product owner increase the product’s price to reflect its higher
value, allowing additional revenues to flow to the income statement.
The second step, realizing value, is often the tricky one. Cutting budgets
and repricing products is not something that just anyone can do. Suppose
the organization is not pushing to cut expenses or is reluctant to increase
prices. In that case, it might be easier for a process owner to just let slack be
absorbed by employees or for a product owner to let the value of product
improvements go home with customers. It might even be desirable to do so
in some circumstances. But if the slack that arises from a data monetization
initiative isn’t removed or if the additional value of a product is not
extracted from customers, the data monetization initiative will not
contribute to the organization’s bottom line.6 The initiative cannot claim to
have monetized data.
Figure 1.3
Three approaches to data monetization
Three Approaches to Data Monetization
There are three distinct approaches that organizations can use to monetize
data, as shown in figure 1.3.
Improving uses data to create efficiencies in work from better, cheaper, or
faster operations. Realizing value requires removing or redirecting the slack
created by the efficiencies that, ideally, flow to the organization’s bottom
line.
Wrapping uses data to enhance products such that customers want to buy
more or are willing to pay more. Value realization requires raising prices or
selling more of the products to improve the bottom line.
Selling is the exchange of an information solution for some form of
money. In this way, realizing value is straightforward and appears in the
form of new financial inflows.
Research to consider
In 2018, 315 executives were asked whether their organizations were
creating value via improving, wrapping, or selling. Across the sample, 50
percent strongly agreed that they were creating value from improving, 33
percent strongly agreed they were creating value from wrapping, but
only 19 percent strongly agreed they were creating value from selling.7
Improving
Improving is the most common way that organizations monetize data, and
examples abound. The United Parcel Service (UPS) used vehicle route data
to optimize delivery routes to save US$400 million annually.8 Columbia
Sportswear used historical package-tracking data to eliminate root cause
issues in their supply chain. This decreased both out-of-stock and
overstocking problems, saving more than US$27 million in inventory
costs.9 Trinity Health used data from smart hospital beds to speed up nurse
response time by 57 percent. Leaders at Trinity Health associated the nurse
response time improvement with a reduction in patient falls, likely
decreasing the cost of patient care.10
Improving is a data monetization approach that generates money
when organizations use data to change the economics of work for the
better and then remove or redirect the resulting slack.
Most organizations have experience using data to improve business
processes and work tasks. Many were inspired by the Business Process
Reengineering (BPR) movement in the 1990s that encouraged organizations
to analyze and design efficient workflows and business processes.11 To
execute BPR, organizations applied technology to clean data and boost its
availability. The data was then used to analyze the root cause of process
slowdowns, to measure the benefits of moving from an old way of work to
a new, more radical way, and to monitor and manage critical process
metrics. The BPR movement convinced many organizations of the benefits
of data-driven process or task improvements. But a negative result of this
movement was that many organizations got in the habit of assuming that the
benefits of process improvement would make their way to their bottom
lines. In fact, it takes organizational attention, resources, and discipline for
financial value to materialize from efforts like those at UPS, Columbia
Sportswear, and Trinity Health.
Quantifying the outcome from improving takes two steps. First,
organizations need to measure the uplift in efficiencies or quality that result
from improving with data. Second, they must remove or redirect the slack
created by those efficiencies or productivity improvements. Some
complications make the value of efficiencies hard to turn into money:
sometimes, an improvement in one process creates efficiencies in
downstream processes; or slack created by an improvement is used to
relieve overworked or stressed employees; or efficiencies gained in a
process show up in increased production or reduced inventory rather than
slack. But if an improvement is supposed to reduce costs or budgets and
they don’t change, data is not being monetized.
Wrapping
The second data monetization approach is wrapping. Wrapping initiatives
create data-fueled features and experiences to increase the customer value
proposition of a product. When we say “product” in this book, we’re
referring to the thing an organization delivers to a customer that satisfies
their needs. This might be a good or a service, it might be virtual or
physical, or some combination of all of these. We use the terms “product”
and “offering” to refer to whatever is delivered to the customer.12 To realize
some of the value created by an enhanced product, the organization must
raise the product’s price or sell more of it. “Selling more of it” could mean
selling more units of the same offering to an existing customer, selling more
related offerings, selling to additional customers, or sustaining sales of a
product whose sales are eroding.
Wrapping is a data monetization approach that generates money when
organizations use data-fueled features or experiences to enhance the
value proposition of a product and then raise prices or sell more
products.
Opportunities to wrap products are everywhere; look around at the surge
in connected devices and the new personalized ways organizations engage
with customers. Some examples of wrapping include adding information to
offerings in the form of reports, alerts, scores, visualizations, or dashboards
that complement or enhance the offering and make it more appealing to
customers.
The wrapping approach to data monetization can create distinctive
offerings in the marketplace. Consider Schindler, an elevator maker. The
company complements its elevators with an equipment performance
dashboard to help building managers monitor elevator performance.
Intergovernmental organizations like the World Bank provide portals for
contributing governments to show them that their donations are achieving
their desired philanthropic goals. Healthcare insurers add visualizations for
health plan administrators to help manage their healthcare costs. In each
case, an offering—an elevator, a philanthropic initiative, an insurance plan
—is wrapped with data, insights, or action to make them even more
attractive to customers (or other constituents).
Any offering can be wrapped, even diapers!13 Pampers is one of several
diaper companies that developed sensors to attach to diapers that send a
mobile alert via an app to parents when the diaper is wet. The app also
tracks information about the baby’s sleep and wake times. It’s hard to
imagine a product that could not be wrapped.
In the digital era, organizations are expected to wrap their offerings using
data-fueled features and experiences that delight customers. But too often,
they assume that they are realizing value from those efforts—without
verifying whether and how much money hits their books. As it does with
improving, data monetization by wrapping takes organizational attention,
resources, and discipline. It requires that organizations first gauge the extent
to which customers regard the product more positively because of the wrap.
For example, do customer loyalty scores go up? Do customers recommend
the product more enthusiastically? Second, organizations need to cash in on
this positive regard by extracting revenues from customers, for instance, by
raising the price of the offering.
There are also complications with data monetization by wrapping:
sometimes an enhanced offering has already been paid for, and the
opportunity to raise prices will only arise in the future. Sometimes
wrapping staves off competitive pressure on prices or increases switching
costs, thus retaining customers who might otherwise have been lost. In
those cases, there might not be an immediate lift in sales but instead a future
lift in sales or dampened sales erosion. However, none of these
complications are reasons to ignore the step of value realization. Wrapping
must make financial sense, or the associated investments might have been
made more profitably elsewhere.
Selling
Companies have been monetizing data by selling information for decades.
Consider the retail industry: retailers have sold their point-of-sale (POS)
transaction data to companies like IRI since the late 1970s.14 IRI, in turn,
sold aggregated data and analytics back to retailers (and other
organizations) that wanted to better understand their product sales
compared to those of their competitors.15 POS data is an important raw
material for the aggregator because they can generate a substantial revenue
stream from it.
Selling is a data monetization approach that generates new revenues
when organizations commercialize data in the form of an information
solution.
Instead of purchasing reports or metrics from the aggregators, some
retailers exchanged their POS data for these solutions. This is bartering,
which is a financial transaction. In fact, both retailers and aggregators must
regularly assess whether the tender they receive for their POS data and
analytic reports justifies the expense or risk of the exchange.
This book will refer to a data product as an “information solution” to
distinguish it from other kinds of products. Information solutions are
standalone offerings that solve compelling customer problems. Customers
might buy an information solution because it includes scarce data that they
need, because it will help them get their own products to market faster,
because of its sophisticated algorithms, or because the interface is user-
friendly, to name just a few reasons. A subscription to Bloomberg news,
data, and trading tools is an example of an information solution. Another
example is IBM’s Weather Company data application programming
interfaces (APIs), which serve climate, environment and forecast data from
a cloud-based platform.16 Note that selling is one of three approaches to
data monetization, yet too many organizations may naively view selling
their data as the only way to monetize it. When organizations limit what
they view as a data monetization opportunity, they end up leaving money on
the table. A lot of money.
The Improve-Wrap-Sell Framework
All three data monetization approaches—improving, wrapping, and selling
—are distinct ways of turning data into money. Their differences are
summarized in table 1.1. Together, the three approaches make up the
improve-wrap-sell framework. Regardless of industry, business model, size,
geographic location, or strategic intent, your organization can monetize data
using some combination of improving, wrapping, and selling. In chapter 7,
you will read about how to choose what combination of approaches is right
for your organization.
Because of the distinctions in how initiatives create value, they require
different capabilities, demand different owners, entail distinct risks, and call
for unique metrics and measurement methods. This will be covered in detail
in chapters 3, 4, and 5. In brief, the success of improving depends on the
leadership of process owners who define needed changes, recognize the
opportunity for applying data, and ensure the adoption of changes.
Wrapping demands engaged product owners who can envision the value
offered by data to their products. They must be willing to engage with other
parts of the company—like IT and customer service—in ways that were not
necessary when developing and selling the core product. Finally, selling
requires the identification of an entrepreneurial leader who can envision and
launch a new information solution for new customers.
Table 1.1
Three approaches to data monetization
Improving Wrapping Selling
Value-
creation
process
Data creates
efficiencies (and thus
slack) by making
operational processes
or tasks better, faster,
and cheaper.
Data
enhances the
customer
value
proposition of
products.
Data is
commercialized
and sold in the
form of
information
solutions.
Improving Wrapping Selling
Value-
realization
process
Slack is eliminated or
redirected.
Customers
pay more or
buy more.
New revenue
streams are
generated.
Measure
value
realization
via
Impact on the bottom line
Who is
accountable
for
outcomes
Process owner Product
owner
Information
solution owner
Key risks Lack of action taking
and value creation
Negative
impact on
customer
value
proposition
when a wrap
falls short
Inability to
create or
sustain
competitive
advantage
Source: Barbara H. Wixom and Jeanne W. Ross, “Profiting from the Data Deluge,” MIT Sloan
Center for Information Systems Research, Research Briefing, vol. XV, no. 12, December 17, 2015,
https://cisr.mit.edu/publication/2015_1201_DataDeluge_WixomRoss (accessed January 10, 2023).
Similarly, the three approaches entail distinctly different risks. The risk
with improving is that the data-insight-action process will break down and
value will not be created. Process owners are ideally suited to manage this
risk by carefully tracking likely value creation and making course
corrections. The risk associated with wrapping is already familiar to product
owners: that the product enhancement adversely affects the value the
customer enjoys from the core offering. Product owners, fortunately,
already know how to track customer satisfaction; they will need to follow
similar protocols when implementing wraps. Finally, the risk associated
with selling is the risk that accompanies any new business venture: business
failure due to the inability to create or sustain competitive advantage.
Information solution owners must remain highly sensitive to competitive
pressures from substitutes and new entrants.
A Final Appeal for the Term Monetization
All organizations should make sure their data investments pay off. And
today, data investments can be enormous. Data investments could simply
make the organization more costly to run if no one is making sure that value
is being created and realized. As a basic business principle, organizations
should generate more money from their data assets than they invest in
producing and managing them. If you buy into the concept of data
monetization but simply don’t like the term data monetization, you are not
alone. It’s distasteful for some. Some organizations, especially
noncommercial ones, have little appetite for the term. This likely comes
from leaders associating data monetization with going too far with data,
with unacceptable exploitation of data assets, or with data trickery or
sneaky tactics.
If you really can’t bring yourself to use the term right now, call it what
you want. Just make sure you use a term that links data to your
organization’s bottom line. It helps to have clear, shared language that
people can use in discourse and debate. If everybody in your organization
uses the term data monetization to mean the same thing, you should be able
to have fewer discussions about whether it’s ethical to monetize data and
more discussions about how to monetize data ethically.
Time to Reflect
The status quo in most organizations is a flurry of data activity but no
coherent vision of what it means to monetize data. Here are the key points
from this chapter to keep in mind:
• As shown in figure 1.2, the fruit (the value) emerges at the end of
the value-creation process. What kind of value do your data
initiatives most frequently create? How well are you measuring
data-fueled value creation today?
• Assuming a data initiative creates some value (there is fruit on the
tree), that value should be realized (the fruit should be taken off
the tree) by moving it to the firm’s bottom line. Do you just
assume that value is realized from your data initiatives, or do you
know it made it to your bottom line?
• There are three basic ways to monetize data: using it to improve
work, using it to wrap goods and services (“products”), or selling
an information solution to someone else. Can you think of an
opportunity to use each one of these approaches in your
organization?
• Improving, wrapping, and selling initiatives should not all be
managed the same way. Who owns your different improving,
wrapping, and selling initiatives? They’re not all owned by IT, are
they?
• Organizations should generate bottom-line impact from their
investments in data. How comfortable is everybody in your
organization with using the term data monetization to describe
this?
Data monetization is an incredible opportunity for organizations. But it’s
not simple. For one, it requires specific capabilities that help organizations
create widely accessible data assets. In the next chapter, you will read about
those capabilities.
2
Data Monetization Capabilities
In many companies, any business unit has the autonomy to hire a firm to [help them]
grab data, toss it in a data store of their choice, and slap an individual use case on top of
it. This happens over and over and over. It takes a different mindset and approach to
build capabilities for consistency across a variety of use cases.
—Brandon Hootman, Caterpillar, Inc.
Why can some organizations monetize their data again and again, while
others have hit-or-miss results? An organization that consistently monetizes
data is leveraging some robust enterprise data monetization capabilities.
This chapter covers how to build the five capabilities that produce data
assets that are accurate, available, combinable, relevant, and secure. Data
assets with these characteristics are easily reused, and reusable data assets
lead to faster and cheaper data monetization initiatives. Organizations
recognized for excellence in data monetization don’t just engage their
advanced capabilities. They also prioritize making it easy for people
throughout the organization to access each capability.
Generally speaking, capabilities are the ability to do something.
Capabilities can exist at a foundational level or they can be advanced. For
example, most home cooks can boil an egg for breakfast; a professional
chef can take an egg and turn it into something unexpectedly delicious. A
diner may have line cooks on staff who consistently prepare a limited set of
menu items, whereas a restaurant known to have an advanced cooking
capability likely has a chef and supporting staff who can create a variety of
sophisticated dishes. The ability to cook at an advanced level is usually
acquired through education and experience, and it no doubt has an element
of talent to it.
In organizations, it’s common to bring people specializing in a particular
capability (e.g., accountants) together. Not only do they learn from each
other as they work together, but everybody in the organization also knows
where to find that capability (in accounting). If the ability to do something
is only needed in select parts of the organization, then the organization
simply needs to establish a local capability. For example, a global company
needs local experts in tax law in each country in which it operates. They are
highly valued in their geography, but their expertise doesn’t need to be
available to other countries.
When a capability is applicable across the organization, it should be an
enterprise capability. For example, an enterprise “digital asset
management” capability allows communications teams worldwide to pull
official brand images from a centralized system. Producing, approving, and
organizing such media content only has to happen once; the images can
then be used and reused in various sales and marketing contexts.
Questions to ask yourself
Can your organization turn data into accurate, available, combinable,
relevant, and secure assets that people can reuse? Or are there skills and
knowledge that are obviously missing?
Research to consider
Organizations who are top performers in data monetization outcomes
have capabilities that are about 1.5 times stronger than those of bottom
performers, and the top performers’ outcomes are 2.5 times better than
the bottom performers.1
The Five Data Monetization Capabilities
Data monetization capabilities are a collection of material resources and
abilities or proficiencies that organizations rely on to develop their data into
reusable data assets. A couple of decades ago, we studied information
businesses like Nielsen and IRI, which rely on data assets for their
economic survival, to understand their business models. It turned out that
the key to these information businesses was that they had five advanced
data monetization capabilities.2 As the research moved on to investigating
data monetization in other kinds of organizations, it became clear that these
five capabilities are key to any data monetization initiative at any
organization. Yes, any organization—including yours—needs data
management, data platform, data science, customer understanding, and
acceptable data use capabilities.
Figure 2.1 shows the five distinct data monetization capabilities laid out
in the shape of a fan. The five capabilities work together. While these
capabilities may not surprise you, they certainly are not easy for
organizations to master.
Here are descriptions of the five capabilities and what it means to achieve
an advanced level of each one. Later in the chapter, you will read about
capability building at the financial services company BBVA.
Data management A data management capability is the ability to
produce data assets that people can find, use, and trust. Organizations with
advanced data management capabilities can report on the accuracy of their
data, match related data entries, consolidate and streamline data fields, and
integrate related data from external sources, such as data aggregators and
suppliers.
Data platform A data platform capability is the ability to capture,
transform, and disseminate data assets securely and efficiently. It leverages
contemporary, cloud-based software to ingest, process, secure, integrate,
and deliver data assets. Organizations with advanced data platform
capabilities can cost-effectively distribute data assets inside and outside the
organization at scale.
Figure 2.1
Five data monetization capabilities
Data science A data science capability is the ability to use scientific
methods, processes, algorithms, and statistics to extract meaning and
insights from data assets. Organizations with advanced data science
capabilities support data-savvy people across the organization in making
evidence-based decisions. They leverage advanced statistics and techniques
such as machine learning to inform and automate processes and products.
Customer understanding A customer understanding capability is the
ability to gather accurate and actionable knowledge about customer needs
and behaviors. Organizations with advanced customer understanding
capabilities accurately grasp what customers need and value, can cocreate
with customers, and can formulate and test hypotheses about customer
preferences.
Acceptable data use An acceptable data use capability is an
organization’s ability to gather, store, and use data assets in ways that are
compliant with existing laws and regulations and consistent with
organizational and stakeholder values. Organizations with advanced
acceptable data use capabilities have contextualized norms and policies.
They have scalable oversight processes, which ensure that employees,
partners, and customers appropriately engage with organizational data
assets.
Capabilities are, by nature, fairly abstract. To make them less ambiguous,
let’s look at the specific practices that build up each capability.
Data Monetization Capabilities Accumulate from
the Practices You Adopt
Data monetization capabilities come mainly through learning by doing, and
so they are shaped by the practices you adopt. For example, when an
organization adopts a foundational practice such as customer journey
mapping, it gradually builds foundational data monetization capabilities,
such as the ability to gather knowledge about customer needs (customer
understanding). Once foundational practices are well established, more
complex practices can be adopted, leading to more learning and higher-
level capabilities. As a result, as illustrated in figure 2.2, as people and
systems undertake more and more complex data monetization practices,
their capabilities get more robust, and the fan becomes fully built out to its
edges. Your organization will progress from foundational to intermediate to
advanced capability levels as it adopts increasingly sophisticated capability-
building practices.3 There’s no shortcut to acquiring robust data
monetization capabilities; they are the result of steady work in the right
direction.
Figure 2.2
How organizations use practices to build capabilities
Take the data science capability as an example: Typically, organizations
first become proficient at basic reporting dashboards and visualization.
Next, they master statistical techniques and approaches. Then they learn
how to use machine learning and specialized analytics like natural language
processing. It’s nearly impossible to fast-track a data science capability by
pumping money into machine learning tools (an advanced practice). At
best, that would yield a pocket of underused machine learning tools that
most of the organization simply would be unable to exploit. An
organization needs to walk before it can run. People need to learn (and
apply what they are learning) as they progress from foundational to medium
to advanced practices.
The next section identifies practices that organizations adopt to build and
invigorate each data monetization capability.4 Practices come in many
different forms. They may be expressed as policies (“Cloud first!”) and
backed up with procedures for minimizing deviations from the policy. They
may be automated (programs for managing access to data), embedded in
tools (statistical packages or AI modeling tools), or expressed as rules and
routines (how customer feedback will be aggregated and shared). As you
will see, there are three levels of practice that have been associated with
building three levels of capability. There are no doubt other practices that
can substitute and achieve similar capability-building outcomes, but
research has validated the practices listed below.
Data management To build a data management capability, organizations
engage in practices that turn data into accurate, integrated, and curated data
assets.
Master data (foundational): Practices that produce reusable data
assets include establishing automated data-quality processes,
identifying data sources and flows that describe core business
activities or key entities like customer and product, creating
standard definitions of priority organizational data fields, and
establishing metadata for those data fields.
Integrated data (intermediate): Practices that allow data to be
integrated from both internal and external sources include
mapping and harmonizing data sources and standardizing,
matching, and joining data fields.
Curated data (advanced): Organizations use taxonomy and
ontology to curate their data. These practices involve analyzing
data and its relationships, depicting data and its relationships in a
way that is accessible and meaningful to users, and maintaining
that depiction over time. These practices make it possible to
augment the organization’s data assets with data assets from
external sources or with data assets created as a byproduct of the
development of AI models.5
Data platform To build a data platform capability, organizations engage
in practices that allow them to draw on cloud, open source, and advanced
database technologies to produce software and hardware configurations that
satisfy their data processing, management, and delivery needs.
Advanced tech (foundational): The adoption of cloud-native
technologies is an example of a data platform practice. Modern
database management tools include products that leverage
leading-edge techniques for data compression, storage,
optimization, and movement.
Internal access (intermediate): The use of APIs to offer data and
analytics services internally is a practice that eases access to raw
data or data assets from any system.
External access (advanced): APIs can also be used to make an
organization’s raw data or data assets available to external
channels, partners, and customers. Providing APIs to stakeholders
outside the organization requires adopting practices for certifying
external users and tracking their platform activity.
To give you a sense of what it looks like to engage in these data
management and data platform practices, consider Fidelity Investments, a
Boston-based financial services company. In 2019, the company kicked off
a multiyear effort to rationalize one-hundred-plus data warehouses and
analytics stores into a common analytics platform.6 Fidelity invested in data
management practices such as creating a common identifier for each major
data entity at the company, for example, customer, employee, and investible
security. It adopted practices for creating definitions of more than three
thousand company data elements and building a central taxonomy and
catalog to organize this new company terminology. Fidelity’s data platform
practices included installing a new modern, cloud-based analytics platform
to house, process, and serve up the company’s data assets to people across
Fidelity.
Data science To build a data science capability, organizations engage in
practices that advance their ability to use data science techniques and
thinking. They hire new talent and upskill and develop existing employees.
They invest in tools and methods that support data science work so that data
science tasks can be appropriately managed and scaled.
Reporting (foundational): Practices that foster the use of
dashboards and reporting include standardizing data presentation
tools and designating which data assets will be regarded as the
“single source of truth” for process outcomes or business results.
They include educating employees about data storytelling and
evidence-based decision-making.
Statistics (intermediate): Practices that promote the use of math
and statistics include selecting analytics tools, hiring people with
sophisticated mathematical and statistical knowledge, and
establishing data science support units. They include teaching
probability, statistics, and skills that increase the usability of
analytics tools and techniques.
Machine learning (advanced): To promote the use of advanced
analytics techniques such as machine learning, natural language
processing, or image processing, organizations engage in feature
engineering, model training, and model management. They use
AI explanation practices that ensure AI models are value
generating, compliant, representative, and reliable.7
Customer understanding To build a customer understanding capability,
organizations connect with customers to collect data about them—
demographics, sentiments, context, usage, and desires—from which they
extract analytical insights about core and latent customer needs.
Sensemaking (foundational): Listening to customers and making
sense of their needs is an example of a foundational customer
understanding practice. Customer-facing employees can help
organizations identify important customer needs by sharing ideas
via “suggestion boxes” or crowd-sourced innovation events.
These employees can also participate in agile or cross-functional
teams tasked with mapping customer journeys or designing new
products and processes.
Cocreation (intermediate): Engaging customers in the cocreation
of new products or new processes requires practices for
identifying the appropriate customer, establishing the terms of
customer engagement, and making good use of customer time.
Experimentation (advanced): Common practices for testing ideas
with customers include hypothesis testing (observing customer
behavior to see if it conforms to expectations) and the use of A/B
testing (using randomized experimentation with two variants, A
and B).
Beginning in 2015, Australian insurer IAG made a significant investment
in its data science and customer understanding capabilities when it acquired
the forty-person customer insights company Ambiata.8 In effect, IAG
acquired experienced data scientists, who brought an influx of data science
practices into the company—statistical techniques, machine learning, and
analytics methodologies. A year later, in December 2016, IAG created a
new division—Customer Labs—that merged experts in data, analytics,
marketing, customer experience, design thinking, and product innovation.
Customer Labs benefited from customer understanding practices that
Ambiata had mastered during its years as a customer insights company, like
A/B testing and experimentation.
It took several years for IAG to diffuse the Ambiata practices across the
enterprise (so that many IAG employees could contribute to and use the
advanced data science and customer understanding capabilities). The
company actively drove diffusion by using its new Customer Labs division
to test and refine Ambiata practices until they were a good fit for IAG.
Acceptable data use To build an acceptable data use capability,
organizations engage in practices that allow them to effectively address
regulatory and ethical concerns regarding data asset use by and about
employees, partners, and customers. Organizations draw on this capability
to mitigate the risk of using data assets inaccurately, undesirably, or in ways
that are not contractually or legally allowable.
Internal oversight (foundational): Practices that ensure acceptable
use of data by employees usually begin with establishing data
ownership; training employees about laws, regulations, and
organizational policy; setting up data access approval processes;
and auditing employee data access.
External oversight (intermediate): Practices that ensure the
appropriate use of data assets by partners begin with establishing
clear agreements about appropriate use with partners and end with
auditing partner use of data assets.
Automation (advanced): Practices that allow customers to self-
manage their data begin with establishing policies regarding
customer control of data. These policies are then implemented
both by communicating the policies to customers and facilitating
customer control through automation. Automating practices also
helps organizations scale internal and external oversight activities.
In 2019, Anthem Health added to its acceptable data use capability by
hiring a technology and governance provider to stand up a cloud-based
environment in which Anthem could collaborate with start-ups, academics,
and others who wanted to use its deidentified patient health data set to
develop and validate AI models.9 There were big issues to resolve: data
access, development standards, intellectual property rights, and more. The
providers technology allowed Anthem to set up base contracts with
parameters that could be tweaked to accommodate the distinct needs of
each partners project; this made the up-front contracting process far more
straightforward.
Figure 2.3 depicts the capabilities fan with advanced states in all five
capabilities. When each capability is developed to around the same level,
the fan could be said to be “well-rounded.” Because the five data
monetization capabilities are highly complementary, they should ideally be
developed to similar levels of advancement. It will be difficult to fully
exploit an advanced data platform without also having advanced data
management and advanced data science capabilities in place. That said, the
five capabilities rarely grow at the same pace. Sometimes an organization
has developed one or two capabilities more fully than it has developed
others, making for a lopsided and possibly ineffective fan. It is likely to be
obvious where additional practices would strengthen the collective power of
the five capabilities.
Figure 2.3
Advanced states of the five data monetization capabilities
Assess Your Data Monetization Capabilities by Assessing Your
Practices
While capabilities are very difficult to measure precisely, practices can be
observed and assessed, and they are a good proxy for an organization’s
capabilities. Consider the earlier cooking example: it’s almost impossible to
look at two people and discern who is the amateur chef and who is the pro.
But you can easily recognize an advanced cook by watching her behavior in
the kitchen: how does she choose a knife, decide when meat is done, and
plate the food? These are actual, observable practices that roll up to a
credible evaluation of a person’s cooking capability.
You can use the capability assessment worksheet in the appendix to
evaluate your organization’s data monetization practices. Follow the
instructions to rate your practices and reveal your organization’s capability
levels.
Enterprise Capabilities Make Initiatives Faster
and Cheaper
Your organization may have considered adopting some of the practices
identified above for reasons of efficiency or cost without realizing their
value in terms of advanced data monetization capabilities. Someone might
push a practice (or policy or rule or process) for other reasons without
realizing that the practice will help build a data monetization capability. For
example, an organization might adopt a “cloud first” policy for financial
reasons without realizing that it is an important foundational practice for
making data assets available for reuse both internally and externally.
Organizations must adopt practices at the enterprise level if they are
going to build enterprise capabilities. Ideally, you want practices to
strengthen capabilities into ones that can be repeatedly shared across the
organization for use by any improving, wrapping, and selling initiative.
Cloud-native applications, for example, let developers quickly create and
deploy individual microservices without disrupting any other microservice;
the developers’ teammates can reuse what works. Adopting enterprise
customer understanding practices ensures that customer insights gained in
one part of the organization are captured and available to other areas. It can
take some time and effort for organizations to migrate local data capabilities
into an enterprise capability (consider how IAG slowly incorporated
practices from its acquisition into the greater IAG company), never mind to
grow new enterprise capabilities organically, from scratch. You can,
however, imagine the payoff as capabilities build up and are available to be
used pervasively and in new ways.
You can think about the value of enterprise capabilities within the context
of racing. Imagine that you own a Formula One racing team. You would
want to race your fancy car on some beautifully engineered track, with
luxurious stands and amenities for spectators, shared and secure fueling,
nicely fitted-out facilities for your pit crew (and your data scientists), and
carefully engineered security barriers to protect your driver. You’d expect
the race host to provide all those shared and reusable capabilities. Your time
would be spent designing the perfect car, finding and motivating the best
driver, and crafting a winning strategy.
In the best of all possible worlds, figure 2.4 shows what data
monetization initiative teams want from their organizations: excellent data
monetization capabilities—great data management to fuel the initiative, a
great data platform for a fast and smooth ride, great data science to optimize
the initiative, great customer understanding to make sure customers are well
served, and great acceptable data use to keep the initiative safely on course.
When enterprise capabilities serve the needs of initiatives, those initiatives
have a smoother, faster path. Teams are free to focus on the specifics of
their initiative: managing stakeholders, developing the team, and training
models.
Figure 2.4
The great enterprise capabilities that teams want from their organizations
Before your organization races ahead to build enterprise capabilities, it’s
important to note that capabilities only create value if they are used. In fact,
organizations can’t take too much time or spend too much money building
enterprise capabilities before they start leveraging them. Enterprise
capabilities that aren’t used are just sunk costs. Nevertheless, an
organization’s long-term goal is the perfect, fully equipped racetrack—a set
of advanced enterprise capabilities—that gets used over and over by many
and varied initiatives.
The reality is that most practices are initially adopted by initiative teams,
so most data monetization capabilities are developed in the context of
initiatives. When enterprise capabilities do not exist, the initiative owner
must unearth the capabilities needed to meet her objectives. Her initiative
team might try out a new tool, experiment with a cloud platform, or work
out an acceptable use policy just because they need to conclude their
particular initiative successfully. However, when an organization adopts the
data monetization practices it needs initiative by initiative, it can end up
with local capabilities that are hard to leverage elsewhere. It takes some
vision and leadership to accumulate enterprise capabilities. This perspective
may be found in the C-suite or a chief data office, where a top-down, global
mindset is expected.
Building Enterprise Data Monetization
Capabilities at BBVA
Let’s look at an example of how one organization built its enterprise data
monetization capabilities to advanced levels over time. BBVA has been
recognized for excellence in generating data monetization outcomes and for
having great data monetization capabilities.10 It did not, however, begin its
journey with its capabilities in an advanced state. In 2011, the 154-year-old
financial services group was challenged with legacy technology that was
costly and slow, employees with outdated data science skills, and heavy
regulatory constraints regarding data use. Yet over time, because its leaders
took a long-term perspective on capability building, the company
established advanced enterprise data monetization capabilities that support
all kinds of data monetization initiatives across its global presence.
Phase 1: Selling Initiatives
In 2011, BBVA leaders were curious about the viability of selling
anonymized bank card data to generate new revenues. They sent a small
team to the MIT Senseable City Lab to come up with information solutions
that some market would want to buy. Leaders gave the team five million
anonymized bank card records, which the team prepared for analysis. This
meant adopting data management practices to clean up the data and to
define fields. It also meant learning how to confirm that records couldn’t be
reidentified and establishing parameters regarding where and how they
could sell the data legally.
At the time, the use of cloud computing at BBVA was banned by
regulators. But while the innovation team was at MIT, it was free to use and
learn about cloud software and services, so it did. It also learned how to use
algorithms and visualization techniques that were far more advanced than
anything that existed within the bank. The team’s MIT experience made it
realize that collaboration with others outside the bank could be very
worthwhile. Learning how to collaborate with entities like start-ups,
government agencies, and philanthropic organizations turned out to be key
to developing meaningful prototypes, which the team then used to
understand what kind of customers would be interested in bank card
information solutions and how much those customers would be willing to
pay for them.
After working with MIT for four years, the BBVA team had successfully
completed several selling initiatives, validating that customers would pay
for carefully analyzed bank card data assets. Also, it successfully
established an initial set of new practices and capabilities that BBVA could
leverage for future initiatives that involved selling bank card data offerings.
It had learned how to identify promising markets that could benefit from
economic impact analyses, such as urban planning and government
agencies.
This effort convinced BBVA leaders that selling data was a viable
strategy. At that point, the leaders set up a legally separate wholly owned
subsidiary called BBVA Data & Analytics (D&A). The new entity was
small, at first only four people, and was expected to become self-funding.
D&A would do this by operating as a separate information business that
sold information solutions based on the bank card data assets that had been
produced during the years of innovating with MIT.
To reinforce D&As autonomy, BBVA located the group in a building in
Madrid separate from the bank. The new physical space was designed to
include contemporary features that inspired collaboration and innovation
(e.g., movable furniture, glass wall whiteboards). The physical separation
from the incumbent bank helped preserve and nurture practices and lessons
gained during the MIT experiences so that new capabilities could thrive.
As a separate information business, BBVA D&A could adopt practices
they deemed worthwhile and build advanced enterprise capabilities required
to sell bank card information solutions. It’s important to note, though, that
the unit was operating at a tiny scale. They offered a small set of solutions
to some narrowly targeted markets, and the resulting revenues were
minuscule compared to the revenues of the incumbent bank. D&As small-
scale adoption of advanced practices translated into incredibly advanced
capabilities but for only a small set of data assets.
Phase 2: Improving Initiatives
As the BBVA D&A data scientists engaged in selling initiatives, they also
networked with data colleagues in the larger parent bank over coffee and
informal lunches. The subsidiary’s data scientists began to realize that
BBVAs internal data efforts could be more fruitful with the more advanced
technology and practices they had adopted. As a result, they offered to help
a few of their bank colleagues approach their initiatives differently. In one
case, BBVA D&A used its more sophisticated data science capability to
help an initiative more effectively optimize the placement of bank branch
locations. The collaboration resulted in US$35 million in cost savings.
BBVA executives were thrilled to learn that data could create so much
value by improving the bank’s operations, but they realized that the bank
needed capabilities supporting different data assets, not the ones the
subsidiary had to offer. In effect, the bank needed capabilities like the
subsidiary but in service to other kinds of data. For example, bank functions
needed data assets for credit risk, customers, website activity, and more, not
just bank card transactions.
To generate more data assets that they could exploit, data scientists from
the subsidiary began advising internal bank initiatives and teaching them
how to use data science tools and techniques. The subsidiary still had to
fund itself, so it hired a financial expert to make sure every initiative it
advised had a stated economic goal and a way to measure its level of
achievement. If returns were positive, the subsidiary pocketed some of the
returns, per its consulting arrangement with the bank. The subsidiary also
absorbed 10–20 percent of the initiative’s costs under the condition that the
initiative contribute to the build out of the bank’s enterprise capabilities. So,
improvement initiative by improvement initiative, the subsidiary helped the
bank accumulate data assets and algorithms, and they pitched the new data
assets and algorithms to new initiatives to encourage reuse.
BBVAs IT group agreed to support the centralized platform over time,
adopting more modern practices for its operation and its oversight. By
2017, the subsidiary had helped the bank deploy more than 40 improving
initiatives for 27 different business units. As a part of those initiatives, the
subsidiary helped unlock 34 new data assets by migrating 188 associated
data tables to a cloud-based enterprise platform that future initiatives could
leverage (thanks to a new cloud policy). Notably, BBVA D&A tracked
capability building as a part of its performance management process. For
example, it tracked how many new assets areas the D&A collaborations
helped to unlock for BBVA as well as other metrics like the number of
reusable machine learning models and the number of BBVA data miners
who had been upskilled to become data scientists.
Phase 3: Wrapping Initiatives
The BBVA D&A data scientists next realized that they could contribute to
the bank’s digital presence by using some of their algorithms and ways of
working to create appealing features for several of the bank’s consumer-
banking products. It was a hard sell, at first, to convince BBVA leaders to
invest in using data science to enhance the customer experience because the
business case was not like that for an improving or selling initiative. So, the
D&A team offered to pilot a single feature, a spend categorizer that would
use analytics to organize a customers transactions and then present the
results in a pie chart. It helped customers understand how they spent their
money. Unfortunately, the initiative took longer than expected. For one
thing, the customer data, a data set they had not previously dealt with,
needed to be cleaned up and made perfectly accurate. To put it another way,
it needed to be turned into a data asset. Also, the D&A team had to learn
how to build and train from scratch an AI model that could categorize
customer transactions.
The spend categorization had to be easily understood by BBVAs
customers; otherwise the feature could do more harm than good. So, the
initiative team learned how to experiment with A/B testing to establish a
mechanism for understanding, over time, how well the feature was meeting
customer needs. Soon after BBVA launched the categorizer, it became one
of the most popular parts of the bank’s digital experience, second only to
money transfers. The resulting feature helped earn BBVA recognition for
best mobile banking in 2017, and the bank won that award for many years
after.
Building Capabilities Means Constantly Adopting New
Practices
When BBVA transitioned from selling to improving, it discovered that its
enterprise data monetization capabilities fell short. Its data assets were
insufficient, the company’s platform could not handle broad internal access,
and local data science skills were outdated. This shortfall happened again
when BBVA introduced wrapping; employees, it turned out, didn’t truly
understand customers because they were using outdated perspectives and
techniques. To some extent, these shortfalls occur because improving,
wrapping, and selling data monetization approaches rely on different
capability profiles.11 Improving, wrapping, and selling each place unique
demands on an organization. For example, organizations focused on
improving business processes will need internally focused capabilities, like
a searchable catalog of shared data terms and definitions for employees to
use to find data assets that can be used for analyses about operations.
Organizations focused on wrapping offerings or serving new customers in
totally new ways will need to focus on governance policies and processes
regarding how employees can and should use customer data assets.
Chapters 3, 4, and 5 will explore the capability profiles for improving,
wrapping, and selling in detail.
Time to Reflect
Here are the key points from this chapter to keep in mind:
• Capabilities are built by adopting practices. Consider your
organization’s weakest capability: What practices does your
organization need to adopt?
• Capabilities are often built to serve the needs of a particular
initiative. Consider your organization’s most robust capability:
How was it built or acquired? What practices contributed the
most to its current state? (If you don’t know, is there someone in
your organization who could tell you?)
• Enterprise capabilities are more valuable than pockets of
capabilities all over the organization. Which of your capabilities is
the most “enterprise,” that is, widely shared? How did it become
an enterprise capability? What tactics have been used to prevent
initiative teams from building isolated capability silos that are of
limited, local value?
• Capabilities only create value if they are put to use. What policies,
habits, or norms do you have in place that will ensure that the
initiatives that are underway find and use capabilities?
• Capabilities produce data assets that are accurate, available,
combinable, relevant, and secure. Data assets with these
characteristics are easily reused, and reusable data assets lead to
faster and cheaper data monetization initiatives. Does your
organization make a distinction between data and data assets?
The nice thing about getting a handle on your capabilities is that you can
spend more time thinking about ways to exploit them! That’s coming next.
In the upcoming chapters, you will learn how you can become great at
improving, wrapping, and selling.
3
Improving with Data
We will gain great efficiencies in the way we operate by having data and technology
help make faster and more informed decisions.
—Robert Phillips, CarMax
Your organization likely has been improving with data for decades. But
even if it’s commonplace for you, you might not be going about it
systematically or strategically. An organization with a state-of-the-art
improving approach can be recognized by (1) its vision for improving, (2)
the amount of value it creates and realizes, (3) the state of its capabilities,
and (4) who is held responsible for improving initiatives.
Does your organization have a specific vision for improving with data?
Do leaders encourage employees to be “data driven” and stop there? Or,
instead, do they encourage the use of data “to move from worst to first” (in
some respected industry-specific ranking), “make every employee one
hundred times faster,” or “eliminate one hundred million hours of wasted
customer time”—or some other compelling goal? Imagine how much easier
it is for employees to use data productively when they know what to aim for
and what will be rewarded!
How well are you creating and realizing value from improving
initiatives? With improving, the value-creation process is under the
organization’s control (unlike wrapping and selling, where customers are
involved). This means the organization can—at least in theory—directly
and actively manage the organizational adjustments needed to ensure that
value is both created and realized.
How strong are your data monetization capabilities? Do analysts take
personal pride in handcrafting byzantine spreadsheets and pivot tables? Do
data miners use software introduced in the age of data warehousing (or
earlier!)? Is data stored hither, thither, and yon? If that feels familiar, it
likely means that your organization needs to update its data monetization
capability-building practices. As you read in the last chapter, any data
monetization approach—including improving—benefits from better
enterprise data monetization capabilities. Better capabilities, better
outcomes.
Who is responsible for your improving initiatives? Is it the “IT
department”? This chapter will persuade you that the responsibility for
improving initiatives, the value they create, the value you realize from
them, and the capabilities you build for them must be shared among a much
larger crowd of stakeholders.
Questions to ask yourself
As you read this chapter, think about an expensive, inefficient, or
ineffective work practice at your organization. Maybe it’s hard to
onboard a customer or respond to supply chain glitches. Can you fix that
problem using data? Do you know how you would measure and monitor
the value created and realized from improving that work practice?
Research to consider
Among MIT CISR survey respondents, improving accounted for 51
percent of their financial returns from data monetization initiatives,
making it the most prevalent of the three approaches in the improve-
wrap-sell framework.1 This likely reflects the maturity of improving in
the marketplace.
Types of Improvements
Most organizations invest in improving initiatives. In a 2019 study of AI
initiatives,2 forty of the fifty-two initiatives studied targeted improving a
process or a task,3 ranging from predicting equipment failure to forecasting
passenger demand to recognizing abnormalities in lab images. For example,
GE created an AI-based contractor assessment for its three thousand
Environment, Health, and Safety professionals that assessed whether a
contractor satisfied GE’s safety criteria. This improvement saved hours of
time during contractor onboarding. (To give you a sense of the scale of this
improvement, at the time of the research, GE was hiring approximately
eighty thousand contractors annually.)
All improvements focus on enabling one of the three steps of the value-
creation process that you read about in chapter 1: they offer data (data
improvements), offer insight (insight improvements), or take some action
that creates value (action improvements). The names indicate the scope of
the improvement. As figure 3.1 illustrates, a data improvement, which just
provides data, relies on the recipient to grasp the significance of the data,
take some action based on that understanding, and create value. An insight
improvement offers task guidance, but the recipient must then act based on
the guidance to create value. With action improvements, the organization is
sure to create the value it set out to produce because the action is taken (or
nearly so) by the initiative.
Improving by Offering Data
Many improving initiatives provide more accurate, timelier, or better
integrated data to a user who previously did not have access to that data or
spent a great deal of time cobbling data together from various sources into a
spreadsheet. One of the supposed benefits of business intelligence reporting
initiatives was that they delivered much better data up and down the
organization. But was that data used productively? Did users know what to
do with it? In a few cases, yes; in many cases, no.
Figure 3.1
How improvements differ in their scope
Sometimes an improving initiative does deliver high-quality data to
decision makers who know what to do with it and are motivated to act.
Many will remember that the US Securities and Exchange Commission
(SEC) failed to detect the Bernie Madoff Ponzi scheme for so long in part
because the numerous tips that citizens submitted went to so many different
people that no one recognized a pattern of concerns.4 To protect against
similar mistakes in the future, the SEC created a single data repository
(TCR) that consolidated tips, complaints, and referrals. This far superior
data source was put into the hands of analysts capable of using it to identify
potential violations of securities laws. Once the analysts surfaced a possible
violation, they were responsible for setting the wheels in motion for further
investigation, which resulted in either TCR resolution or enforcement
action (i.e., creating value from the TCR data). The key was getting the
right data into the right hands.
But more often, an organization has to do much more than deliver high-
quality data to smart decision makers. Organizations also must actively
ensure people can and are willing to engage with that data. Users cannot
engage when they are poorly trained. (Data literacy programs and analytics
training are great ways to address such skill hurdles.) Also, users will not
engage when they are distrustful or too busy. If you choose just to offer
data, make sure you monitor that data’s use as well as the steps between
use, value creation, and value realization.
Improving by Offering Insight
Improving initiatives can offer insights in the form of benchmark scores,
exception reports, advice, and different kinds of visualizations and alerts.
While offering insight in no way guarantees the use of insight, it is at least
one step closer to value creation.
At contemporary apparel retailer GUESS, the data science team won over
the company’s creative staff (clothing buyers and designers who had little
time or inclination to embrace insights based on data) by giving them cool
devices, hiring a graphic artist to develop a fun, contemporary app
experience, and creating visual dashboards that included photos of apparel
and store layouts.5 As a result, the buyers and designers began using
insights about top-selling fashions, regional demand, and effective
merchandising. They saved time previously wasted in making sense of
tabular reports with cryptic SKU numbers because everybody was “already
on the same page” regarding important product sales trends. Based on the
accessible and consumable insights, they focused their time on developing
and deploying new selling, demand management, and merchandising
strategies (i.e., taking actions that created value).
As a rule, insights are most likely to lead to action taking and value
creation if they are delivered to people responsible for acting. Most of the
forty AI initiatives mentioned earlier delivered insights to experts of some
sort. For example, equipment failure predictions went to individuals
responsible for taking equipment offline, not to a machine operator;
passenger demand forecasts went to people responsible for changing flight
schedules, not to caterers; alerts about abnormalities in lab images went to
radiologists, not nurses. Insights must be delivered to people (or systems)
who have the authority and ability to act on them.
Improving by Triggering or Prompting Action
As you might suspect by now, you can avoid the risk of a breakdown in the
value-creation process by triggering or prompting action. Many AI-based
improving initiatives trigger the automatic execution of some tasks. In the
study mentioned earlier, one-third of the AI improvements involved
automation. They included action taking like automatically remediating
network security breaks, automatically reordering items that were low in
stock, adjusting equipment settings after changes in the operating
environment, and automatically sending an email containing ideal
marketing content to a customer.
Full automation is not always easy to achieve because so many
complementary organizational adjustments need to happen. Consider the
case of Trinity Health, one of the largest healthcare delivery systems in the
US.6 An upcoming remodel for a flagship hospital provided an opportunity
to pilot and execute IoT-enabled use cases, including one that helped nurses
respond faster to patients and reduce patient fall risk. The improving
initiative automatically sent an alert to a nurse’s mobile device if a patient
with a high risk of falling started to get out of a sensor-laden bed so the
nurse could respond faster. Before the alert could be automated, the team
had to do many things: establish business rules that spelled out exactly who
to alert, in what order, and under what conditions; clean up the patient data
and create accurate fall risk scores; redesign the process by which nurses
made rounds; educate staff to follow the new processes and procedures; and
create incentives that would get staff to buy into the new policies and
procedures. After the initiative was deployed, Trinity leaders viewed this
use case as a success. They created value in the form of a 57 percent
reduction in nurse-call response time, which correlated with fewer patient
falls.
In some initiatives, triggering or prompting action does not mean
completely automating it but just making human action simple and
straightforward. For GE’s AI-based contractor evaluations, for example, a
professional pressed a button to initiate evaluation assistance. The AI model
then analyzed the document and reported on whether the GE safety criteria
were satisfied or not. The application offered easy access to the rationale
underlying the AI assessment, which allowed the professional to quickly
accept most of the assessments and move on, creating significant
efficiencies in the process.
Creating Value from Improving
When it comes to creating value from improving, initiative teams must first
articulate the type and magnitude of value that the initiative intends to
create. Then, they must make any complementary organizational changes
that are needed to ensure that value gets created.
In a sea of possible improving outcomes, organizations must clarify up
front what kind of value they most want to generate from an initiative. At
times, initiative teams aren’t sure if the type and magnitude of desired value
is possible. In these cases, they draw heavily on pilot testing and
experimentation. They can zero in on an initiative’s value potential by
investigating an improvement at a small scope or in a controlled manner.
Pilot tests allow the organization to establish value baselines (e.g., the pre-
improvement level of productivity of a process). Experiments often require
developing a measurement approach for tracking value creation that,
fortunately, can be sustained after deployment and over time.
For example, leaders at Trinity Health also wanted to know up front what
kinds of value they could create from improving initiatives (like the nurse
response initiative) in a “smart” hospital room. Specifically, they wanted to
improve the quality and efficiency of patient care. But in what ways? The
leaders asked a pilot team to fit thirty patient rooms with sensors
everywhere: in medical devices, beds, patient wearables, and strategic
locations like doorways and handwashing stations. In one pilot, the team
tested whether monitoring hand hygiene would lead to better infection
control outcomes (improving by offering data). Data from handwashing
sensors and staff-location sensors was used to monitor handwashing as care
providers entered a hospital room. Analysts then calculated handwashing
percentages and correlated those with hospital-borne infection rates.
These results were shared with managers who were responsible for staff
handwashing practices. The pilot test results justified broader deployment
of the hand hygiene initiative and helped the initiative team establish
realistic value-creation goals. Three years after the pilot, Trinity Health had
recorded over 14.5 million hand washes, representing increased adherence
to hand hygiene procedures within medical-surgical and critical care patient
areas. This adherence had led to a 29.7 percent reduction in C. difficile
infections and a 24.5 percent reduction in MRSA infections.
Regardless of whether an organization deploys an improving initiative
that offers data or insight or triggers or prompts action, the complete data-
insight-action value-creation process must happen for value to materialize.
The top risk to value creation with the improving approach is not making it
to action. One way to mitigate this risk is to extend the initiative’s scope (as
in figure 3.1). For example, once the SEC had its TCR database in place, it
could streamline the work of analysts by providing them with actionable
insights. That is, the initiative team would increase the scope of the
improvement from offering data to offering insight. And at GE, until their
contractor evaluation application served up easily accessed, clear
explanations for the AI model’s assessment, evaluators were more likely to
ignore it and instead perform slow, manual evaluations. The GE initiative
team increased the scope of the improvement from offering insights to
prompting action.
Because so many inaction-risk challenges can pop up, it is important to
be keenly aware of how the value-creation process is expected to unfold. If
possible, the process should be monitored by either instrumenting data or
insights to see if they are used or by asking users directly about use,
periodically. Are there obstacles related to the ability of decision makers to
use data that could be resolved with training or assistance? If insights are
not being acted on, perhaps the insight is going to the wrong person, not
someone with the authority and ability to act on it. As you saw with some
of the improving examples, to make sure that an improving initiative
creates value, it might be necessary to change related policies or business
rules, redesign processes, change how data is collected, redesign jobs,
change performance measures or incentives, and reskill or replace people.
Realizing Value from Improving
You already know from chapter 1 that organizations must stay on top of
value realization from any initiative. And you know that improving
initiatives often seek to standardize or simplify processes and work tasks,
creating value in the form of efficiencies. For the organization to realize
value from efficiencies, the resulting slack must be redirected or removed.
That is, if the efficiencies reduce the need for headcount, the equivalent
headcount must be removed from the process or redirected to other work.
The savings flow to the bottom line. At GE, for example, the AI
improvement initiative reduced the time GE professionals spent reviewing
documents in the office, freeing them up to focus on higher-value work, like
getting into the field to look for and solve safety problems. However, keep
in mind that some slack is a good thing. Slack allows for innovation and
also helps organizations deal with environmental uncertainties, such as
sudden increases in demand.7
What was not mentioned in chapter 1 is that an improving initiative can
also create value by making a process more productive or by improving
product quality. The value created by producing more and better products is
realized—turned into money—by selling them. Or, as in the case of GUESS
and the insights for clothing buyers and designers, the value created in the
form of better product placement was realized in the form of increased
revenues from fewer discounts and markdowns. In other cases, realizing
value from better products might require price increases. In chapter 4, you
will read more about realizing value from product sales, since that is the
dominant value-realization mechanism for wrapping initiatives.
Figure 3.2
Value realization from improving initiatives
To summarize, as shown in figure 3.2, some of the value created by the
improving initiative is realized by the organization as reduced costs, and
some is realized by the organization as increased revenues. And some value
is left on the tree, so to speak—some value has been created that is not
turned into money. It might take the form of slack capacity to be used for
innovation, it might reduce pressure on employees or managers, or it might
go home with customers. The amount of value left on the tree could be
considerable if slack is not removed or redirected.
Improving at Microsoft
Microsoft, one of the world’s most recognizable technology companies,
exemplifies a company that invested big in improving, with initiatives of all
shapes and sizes. Though headquartered in the US, in 2022, the company
operated globally, employing more than 163,000 people. Despite ongoing
economic shocks and changes in consumer behavior, by June 2022,
Microsoft had grown to a market capitalization value exceeding US$2
trillion.8
In 2014, the company faced increasing competition from the likes of
Google, Apple, and Oracle as well as significant shifts in consumer
behavior and expectations. In particular, the software industry’s movement
to cloud-based services called for a substantial change to Microsoft’s
business model: offering cloud services required constant information about
service usage, a deep understanding of customer attitudes and needs, and
entirely new pricing and selling strategies.
Incoming CEO Satya Nadella embraced this challenge in 2014 with
outstanding results. Just three years later, cloud revenues had grown by 93
percent, and Microsoft’s share price had more than doubled. Also, 61
percent of Microsoft employees were using data and analytics monthly.
Nadella’s clear vision, combined with the continued prioritization of
evidence-based decision-making, resulted in a cascade of internal
improvements that transformed Microsoft into a data-fueled organization.
Microsoft engaged in improving with an explicit vision. Like many
CEOs, Satya Nadella used the term “data-driven” a lot as he talked with
audiences both inside and outside the company. But it was obvious what
Nadella meant by the term. He meant he wanted Microsoft employees
everywhere, around the globe, to use data to change the very nature of their
work so that the company could successfully transition from a software
product company into a cloud-based services provider.
To back up his words, Nadella made bold moves. He consolidated core
business functions (e.g., sales, marketing), breaking down product-oriented
silos. He adjusted employee incentives so that one of the three core pillars
of assessing an employee’s performance was how well they collaborated
across the organization. And he set a goal that every Microsoft employee
would use Power BI to perform work. Collectively, these efforts created an
environment in which leaders could create value from data. As a result,
improving initiatives sprang up across Microsoft.
Let’s look at one example of an improving initiative championed by
leaders in finance (specifically one that offered insights). The leaders
wished to raise the effectiveness of Microsoft’s financial analysts by
reducing the cycle time between financial analysis and field action. The
goal of the initiative was for analysts to spend less time analyzing financial
data and more time communicating insights to their partners in sales. The
finance team adopted a suite of comprehensive, flexible analytical tools so
that analysts could answer a wide range of questions on the fly during
business reviews and answer impromptu questions from sales personnel. To
hone the analysts’ communication skills and presentation techniques,
leaders established a storytelling training program for them that included
webinars, live demonstrations, videos, and in-person sessions. Within
fifteen months, these efforts helped financial analysts reduce their time to
produce insights by 30 percent and to redirect that time to sales partner
communication. These outcomes aligned with Microsoft’s need to create
entirely new pricing and selling strategies as it shifted to its cloud services
business model.
This short example demonstrates some key points about the improving
approach to data monetization:
• Nadella’s vision to shift to cloud services demanded significant
efficiencies that would allow effort to be redirected toward sales.
• The example initiative made Microsoft’s existing financial analysts
more efficient by decreasing the time required to produce
financial analyses.
• Analysts were also made more effective in that they could deliver
more meaningful insights and immediately respond to their sales
partners’ queries.
• Finance leaders (i.e., the owners of the business process being
improved) were accountable for ensuring value creation and value
realization.
• To support the value-creation process (i.e., increase the likelihood
that salespeople would act on the analysts’ insights), finance
leaders established a storytelling training program that taught
analysts how to present actionable insights in a compelling
manner.
• Finance leaders measured success (i.e., value creation) as the
reduction in data-gathering time and corresponding increases in
time devoted to sales partner communication.
• Value realization occurred when the time saved by analysts was
redirected to the more valuable activity of supporting their sales
partners who were growing sales.
Again, this is just one example of improving at Microsoft after Nadella
came on board as CEO. There were many other new improving initiatives
throughout the company.
Data Monetization Capabilities for Improving
Chapter 2 explained that five data monetization capabilities, namely data
management, data platform, data science, customer understanding, and
acceptable data use, power data monetization initiatives. Like any kind of
initiative, improving initiatives with access to more advanced capabilities
are associated with greater data monetization returns.9
If you are curious to know how advanced capabilities need to be,
research indicates that organizations classified as top performers in
improving (those that reported top scores in improving outcomes) have the
distinct pattern of capability practices illustrated by figure 3.3.10 (As you
will see in the next two chapters, top-performing wrapping and selling
organizations have their own distinctive patterns.)
Figure 3.3
The capabilities of top-performing improving organizations
While the organizations identified as top performers in improving have
better capabilities than bottom performers (whose fans would be blank),
they do not necessarily have advanced data monetization capabilities. They
do, however, possess the following capabilities, which foster their ability to
achieve their improvement goals:
• These organizations have accurate master data, particularly about
operations, such as the chart of accounts, product or part numbers,
employee identifiers, location codes, or asset identifiers.
• They provide internal access to data and tools through a data
platform built using cloud and advanced technology, making data
access fast, ubiquitous, and cost efficient.
• Their data science capabilities are well established at the statistics
level, allowing them to provide insights necessary to optimize
processes and tasks.
• A basic level of customer understanding is undoubtedly important
for improving initiatives so that changes are made in line with
customer needs. However, on average, top-performing improving
organizations do not have a notable level of this capability. This is
probably because many improving initiatives are not customer
facing.
• Internal use of sensitive proprietary data is formally monitored and
governed to ensure appropriate data use.
In sum, top-performing improvers draw on capabilities that help them
produce data assets that can be reused in ways important for them.
Data Monetization Capabilities at Microsoft
People often assume that technology companies are great with data. But just
like any organization, technology companies can establish capabilities
locally instead of establishing data monetization capabilities at the
enterprise level. They can also become comfortable with established ways
of working with data—using spreadsheets and SQL queries—at the expense
of introducing new tools and training.
This was certainly the case at Microsoft before Nadella’s appointment.
However, as Microsoft leaders and employees came to rely more on data,
chief information officer (CIO) Jim DuBois established four corporate
shared services groups to amass enterprise data monetization capabilities.
These corporate groups worked closely with leaders across the organization
to establish practices needed by new data monetization initiatives.
To illustrate the interplay between capabilities and initiatives, consider
another improving initiative, one that streamlined Microsoft’s sales
processes. Sales leaders set out to increase the time salespeople spent with
customers by 30 percent, or 1.5 more days per week. This goal was again
driven by Microsoft’s business model shift, which required salespeople to
understand customer attitudes and needs more deeply.
Microsoft initially lacked the necessary data management capability for
this initiative; critical data was buried in siloed, product-line applications,
with inconsistent data definitions and coding conventions. Salespeople drew
on data from more than eighty different systems. They had to contend with
multiple definitions of the term “sales lead,” forcing them to waste time
manually translating and integrating the information they needed. As a first
step to a better data management capability, sales leaders adopted a process
for standardizing shared sales concepts—such as what is meant by
“pipeline” or “lead”—so they could build consensus on the agreed-upon
definitions among salespeople, sales managers, and IT.
To provide salespeople around the globe with access to the new and
improved sales data, Microsoft had to modernize its data platform
capability. The company invested a year in developing the Microsoft Sales
Experience platform using Microsoft Azure cloud technology. The platform
team identified the key source systems from which to pull data and
established data movement processes; standardized fields so that sources
using different field identifiers and formats could be integrated; and
established reference data (such as a standard list of country codes) to
maintain consistency in the values of commonly used fields. The resulting
platform ingested and consolidated sales data to produce 360-degree views
of Microsoft’s relationships with corporate customers. For each customer,
this new system summarized purchases, issues and complaints, and
previous communications.
The platform hosted a collection of dashboards driven by Power BI
(Microsoft’s own business analytics service) and other data services.
Process designers arranged these dashboards into workflows to support
different sales personas, such as sellers and sales managers. Each unique
workflow offered users helpful access to the company’s data science
capability; the salespeople accessed information and actionable insights that
were specific to their accounts and work tasks.
Because this initiative involved improving key customer-facing
processes, a customer understanding capability was needed. (The earlier
Microsoft example of improving the efficiency of financial analysts did not
depend on a customer understanding capability.) They adopted a practice of
drawing on the collective knowledge of the sales force, a foundational
practice called sensemaking, or listening to customers and making sense of
their needs. Salespeople provided input into workflow ideas, reporting
requirements, and even features that could be used to feed sales-related
machine learning models (e.g., a model to predict the likelihood of a deal
closing). Their input, which in effect was infused into new work tools and
tasks, helped Microsoft build a customer understanding capability.
Finally, Microsoft developed its acceptable data use capability by
monitoring dashboard usage and being transparent about this oversight. The
company remedied barriers to data access and curbed inappropriate use by
providing better support, training, and incentives.
Microsoft’s data monetization capabilities turned Microsoft’s sales data
into data assets reused by the reengineered enterprise sales process (an
improving initiative that prompted salesperson action). To track value
creation, sales leaders monitored their employees’ use of dashboards and
measured the decrease in time spent on administrative duties. To realize
value, leaders encouraged salespeople to reallocate their time (on average,
1.5 days per week) to customer-facing activities. In effect, from this
improving initiative, sales leaders realized value equivalent to avoiding the
cost of increasing the size of their trained and experienced salesforce by 30
percent.
Ownership of Improving Initiatives
The ideal leader for an improving initiative is the owner of the process,
activity, or task being improved; for simplicity, let’s refer to improving
initiative owners as process owners. A person in this role is accountable to
the organization’s leaders for a process or task outcome that influences the
organization’s bottom line (e.g., the cost of making something, the speed at
which something is made, and the quality at which something is delivered).
She understands what makes her process tick, how process tasks are
accomplished, and what information is relevant to the work being done. The
process owner also understands how the performance of her process relates
to or impacts the organization’s key performance goals.
At Microsoft, the owner of the sales improvement initiative was the head
of Microsoft’s enterprise sales business unit (not someone in IT, on the
process design team, or leading data). Only this person was in a position to
ensure that the desired value was created (an average reduction of 1.5 hours
per week of administrative work) and to manage the risk of inaction. The
head of sales drew on his power, clout, and control over resources to keep
the initiative moving along. And he was perfectly positioned to redirect any
slack created as a result of the initiative to other sales unit needs, as he did.
There are cases in which process owners need help from other leaders to
realize value from productivity or product quality gains or to realize value
from efficiency gains that arise in downstream processes because of
improvement to the processes they control. For example, the owner of
Microsoft’s financial analyst improvement initiative, the person in charge of
financial analysts, had to work with another leader—who had oversight of
field sales—to make sure that financial analysts’ efficiencies ultimately
translated into their sales partners’ generating additional sales.
As you might sense, process owners—and other leaders on whom they
rely—are vital for improving initiatives. But it takes a village for
improvements to succeed. For example, at Microsoft, the entire global
workforce was expected to engage in new ways of work and use data
whenever possible for tasks of any kind. At Trinity Health, smart hospital
room efforts unfolded with the help of IT people, data analysts, clinicians,
and all levels of hospital staff. At GUESS, improvements to selling, demand
management, and merchandising evolved through collaboration among app
programmers, graphics designers, data teams, store employees, operations
teams, and increasingly engaged buyers and designers. In fact, in the
improving examples throughout the chapter, myriad people from all levels
across the organization have been responsible for some facet of data
monetization. Improving, as it turns out, is everybody’s business.
Time to Reflect
Regardless of their size, organizations that wish to explore improving
initiatives should begin by considering their vision for improving, the value
they intend to create and realize, their capabilities, and whom they will
involve in improving initiatives. Like Microsoft’s Nadella, leaders must be
clear about how data assets will be used to improve operations and generate
value. Organizations must also understand what data monetization
capabilities are required to support their improvement goals and how they
will be developed. Finally, for any improving initiative, leaders must
designate a process owner with accountability for the initiative’s success,
who will be expected to share responsibility broadly among the many
people who need to be engaged.
Here are the key points from this chapter to keep in mind:
• Improving initiatives deliver (1) data or (2) insight to decision
makers, or they prompt or trigger (3) action. Which kind of
improving initiative (data, insight, or action) is easiest for your
organization?
• Improving initiatives don’t create value until some action takes
place. For your improving initiatives that deliver data or insight,
how well is your organization tracking action and value creation?
• It is crucial that value created be realized and reflected in your
bottom line. Does your organization realize value from its
improving initiatives? Or is it leaving money on the table?
• Improving initiatives draw on all five capabilities, not just data
management. Looking back, can you think of an initiative that
failed because the capabilities it needed were not available?
What capability-fostering practices did that initiative require?
• Improving initiatives should be owned by people who can ensure
that value is created. Looking back, can you think of an improving
initiative that did not create value because the wrong person
owned it? Who should have owned that initiative?
Ultimately, improving is a great place to start if your organization is just
beginning its data monetization journey. For organizations that have
mastered improving, the subject of the next chapter—wrapping—is a
natural next step.
4
Wrapping with Data
Companies that create enhanced customer experiences using proprietary data will limit
the threat of substitute products and thereby create sustainable margins.
—Gregg Jankowski, AlixPartners
The last chapter explained how you can use data to improve work practices
so that your organization produces more or better output at less cost. As you
were reading chapter 3, you were probably focusing inward, thinking about
how your organization functions today. Now it’s time to turn your attention
outward, focusing on enhancing what your organization produces as
perceived by your customers or constituents. Data wrapping is all about
your customer or whomever your organization serves.
When you use data to create a feature or experience for a product with
the implicit objective of delighting the customer, you’ve created a wrap.
You aren’t generating a standalone information solution; you’re enhancing
the value of an underlying offering. The product can be physical (a tractor),
intangible (a bank account), service based (a taxi ride), noncommercial (tax
servicing), or for profit (freight delivery). All of these products can be
wrapped. For example, a tractor can be wrapped with a digital display that
shows operational performance; a bank account can be wrapped with a
chart that categorizes the account owners spending; a taxi trip can be
wrapped with a fare estimator; a tax form can be wrapped with
prepopulated fields; and freight delivery can be wrapped with notifications
of expected delivery times. These features and experiences offer exciting
possibilities if your organization struggles with product commoditization
and rising customer expectations. In a competitive environment, wrapping
can help your offerings stand out in the marketplace.
Perhaps your organization feels pressure to give customers and
stakeholders more. You might have efforts underway to map out customer
journeys, find and fulfill unmet customer needs, or engage in customer
cocreation. If you do, it’s time to consider data wrapping and tap into a
world of data-fueled ways to change or freshen up your offerings.
Questions to ask yourself
As you read this chapter, reflect on the friction your customers—or
constituents—experience with your current offerings. How can you use
data to make your products more useful, easier, or more fun to
experience? Can your offerings do more to help your customer save
money, make money, or achieve a goal that matters to them?
Research to consider
In 2018, of five-hundred-plus product owners surveyed, 85 percent had
wrapping initiatives underway, and 55 percent of those wraps were
already deployed in the marketplace.1
Types of Wraps
Organizations today must be able to walk in the shoes of their customers.
This is true whether they are working hard to sell goods or services, to
achieve a philanthropic mission, or to serve the needs of a constituency of
citizens. Delivering effective and enjoyable offerings is only possible with a
deep understanding of customer needs and the extent to which customers
perceive those offerings are meeting their needs.
By listening to their customers, organizations might learn that their
offerings are inconvenient to buy, cumbersome to use, or hard to return.
Wraps to the rescue! Wraps can help customers at any point along the
customer journey. Wraps that help a customer solve a problem related to the
offering make the offering more valuable to the customer. Most websites
and apps deliver wraps that enhance core offerings. Consider an app for a
meal kit service that helps a customer choose suitable meal options, manage
nutritional intake, and identify optimal recycling options. These
informational enhancements are wraps that help the customer better
acquire, use, and retire their meal kits.
Wrapping can add value to offerings in both business-to-consumer (B2C)
and business-to-business (B2B) settings. For example, chapter 2 recounted
how BBVA offered a spend categorizer to consumer-banking customers to
delight them and get them to use their bank cards more often. The wrap
used machine learning algorithms to sort customer transactions into rent,
food, and other budget categories and then displayed a customers spending
activities as a simple chart. The bank promoted the categorizer as a way for
BBVA consumer-banking customers to manage their financial health.
Later, BBVA also created wraps for business customers. The bank
offered store activity dashboards to merchants that purchased BBVA POS
services. This dashboard wrap drew on data collected, anonymized, and
aggregated from BBVA bank card transactions and POS terminals. It
displayed insights and alerts that answered common merchant questions
like, “How much total revenue is my business generating compared to the
average in my business sector?”
All wraps fall into one of the same three basic types that improvements
did. They offer data to the customer (data wraps), offer insight to the
customer (insight wraps), or take some action that benefits the customer
(action wraps). With wraps, the customer achieves a goal by taking an
action as a result of being presented with data, an insightful analysis, or an
action trigger. And by taking action, customers create value for themselves.
Whether and how the organization realizes value from the wrap is a topic to
be discussed later.
Figure 4.1
How wraps differ in their scope
The names for data, insight, and action wraps simply describe the scope
of the wrap in the value-creation process. A key difference in the three
types of wraps is the organization’s visibility into the customers value-
creation process. As figure 4.1 illustrates, the wrap that simply provides
data leaves insight finding and action taking to the customer. The
organization has little visibility into whether and how insight and action
happen and how much value is created for the customer. An insight wrap
points the customer in the right direction, but the customer must take the
action that creates value. With action wraps, customer value creation is all
but guaranteed because the wrap is designed to trigger action. Insight and
action wraps can be instrumented to give the organization some visibility
into use but not necessarily into value creation.
All of this should sound familiar to you—unless you skipped the last
chapter. The important twist in this chapter is that wrapping is intended to
help your customers achieve their goals, not to help you and your
colleagues achieve your goals.
Data Wraps
Data wraps offer customers data that can take many forms: simple reports,
dashboards, charts, and even data feeds that customers can integrate into
their own systems. For example, social media companies give advertisers
simple reports detailing how consumers are responding to their ads.
Advertisers can use this information to review the success of their ads and
withdraw, adjust, or expand their social media advertising buys. Some local
governments give citizens simple reports that reflect a household’s status
with the municipality. Citizens can log in to a portal to assess their
compliance with local obligations like dog licensing and their standing with
local services like garbage pickup.
Compared to the other types of wraps, data wraps do the least work for
the customer, so they have the weakest association with customer value
creation.2 That is, data wraps are the least likely to generate customer value.
However, one attraction of data wraps is that they require the least effort to
launch.
Insight Wraps
An insight wrap simplifies customer decision-making or problem-solving
with respect to the core offering. Wraps that offer insights provide
customers with the next steps to take, recommendations, flags for irregular
activities or unusual data patterns, benchmarks, or alerts.
A food and beverage provider offered a party planner chatbot to party
hosts to prevent over- or underspending on drink and snack purchases.3 The
chatbot used advanced analytics to analyze historical sales data and
generate optimized shopping lists for the hosts based on the type of party
and the number of attendees. Those party hosts who followed the
recommendations created value for themselves by throwing a less costly
party. It’s worth noting that not all party hosts will do this! As an insight
wrap, the chatbot relied on the host to follow through and make the
recommended purchase. Some hosts might have second-guessed the list,
restricted their purchases to items for which they had coupons, or ignored
the list entirely. For all those party hosts, the chatbot would not have lived
up to its value-creating potential.
Insight wraps get organizations one step closer to customer value creation
than data wraps; they point customers to possible solutions that could
generate value for them. However, for customer value to materialize, these
wraps need to deliver insights that customers can understand and act upon
to achieve their own goals. As a result, insight wraps require more
advanced data monetization capabilities than data wraps, particularly more
advanced data science skills and a deeper understanding of customer wants
and needs.
Action Wraps
If the food and beverage providers chatbot had ordered the items on the
shopping list for the party host, then it would be an action wrap. It would be
taking action on behalf of the party host. Imagine if the chatbot could detect
the party host’s current location, discover which stores in the area had the
shopping list items in stock, order them, and schedule the order for drive-
through pickup. Now that would be an action wrap!
Action wraps typically include analytics features that first determine
what change in the customers situation is needed. Then the wrap comes as
close as possible to making that change happen. There are some interesting
action wraps in IoT contexts. For example, one farm equipment provider
placed sensors on its equipment and collected data to monitor performance
(device status, temperature) after it was installed on the customers
premises. The equipment provider created an action wrap that predicted
potential equipment failure, ordered parts, and scheduled a service call on
behalf of the customer.
Sometimes action wraps require the customer to initiate the final action,
but they make it very easy to do so. BBVA created an app feature that
notified a customer about a refinancing opportunity that appeared to fit their
needs. It offered to connect the customer with a live financial advisor who
was teed up to support the customer with a refinancing option. The feature
laid the groundwork, but the customer made the final move with a click.
So, if action wraps are so great, why don’t organizations always create
wraps that act? Well, often, they can’t. They might not be confident what
action should be taken or be allowed to act for regulatory reasons. They
might also not have suitable systems or processes in place, or their
customers might not want them to act. For these reasons and more, data and
insight wraps might have to suffice. But regardless of the kind of wrap the
organization creates, it’s always important to keep the customers final
action (or actions) in mind. What will the customer do with the data or
insight provided in the wrap? What kind of value—and how much value—
will that action create for the customer? As you will see later, the amount of
value the customer creates from the wrap puts a ceiling on the amount of
money the organization can ultimately realize.
Characteristics of Great Wraps
Wraps that customers find useful and engaging are more likely to increase
unit sales, command higher prices, grow market baskets, and improve
customer retention.4 Useful and engaging wraps have four characteristics:
They anticipate, meaning the wrap understands the customers need in
advance. They adapt, meaning the wrap meets the customers need in a
tailored way. They advise, meaning the wrap supports evidence-based
decision-making. And they act, meaning the wrap performs an action that
benefits the customer. Figure 4.2 illustrates these four characteristics, called
“the four As.”
Let’s take BBVAs spend-categorizer wrap as an example. In 2016,
BBVA was first to market with its pie chart of categorized expenses, so it
captivated customers with its novelty. But today, that original pie chart
would not get high marks on the four As. It would get low marks for
anticipate because it was inherently backward-looking; it simply showed
what a customer had already spent. The early pie chart was somewhat
adaptive in that it had a few tailoring features based on customer needs or
preferences. It would get low marks for advice because the pie chart didn’t
help the customer decide what to do about the information. The pie chart
would also get low marks for act; the pie chart certainly did not take any
action on behalf of the customer.
Figure 4.2
The four characteristics of useful and engaging wraps
Intuitively, an insight wrap should earn high scores on advising, and an
action wrap, of course, should get high scores for acting. The spend
categorizer is a classic example of a data wrap. Data wraps, by their nature,
primarily rely on features that adapt. They draw on customer-specific data
and generate information tailored to the customer at hand.
Higher scores on the four As will increase data monetization outcomes
for customers (in value creation) and organizations (in value realization), so
it’s only natural for organizations to evolve data wraps into insight wraps
and then into action wraps over time.
Anyone who looked at BBVAs financial manager app five years after
launch would have appreciated how far its features had come since the early
pie chart days: BBVA customers could view predicted expenditures for the
upcoming two months so that bills or payments wouldn’t catch them off
guard; that would earn this wrap a high score for anticipate. Customers
could set spending targets, and the app would help them stay under the
targets by alerting them before they reached a cutoff level; that would earn a
high score for adapt. Customers could see, on average, what people in their
neighborhood—presumably, people like them—were spending on things
like utilities and food, which could lead them to rethink their spending; that
would earn a high score for advise. Finally, as described earlier. the app
could alert the customer to a new refinancing opportunity, connecting the
customer with a loan agent with the push of a button; that feature would
earn this wrap a high score for act.
In sum, the four As are a checklist that helps gauge a wrap’s potential for
creating customer value. Organizations can score a wrap on the four As to
determine how useful and engaging a wrap is likely to be. By comparing
the scores of various wrapping proposals, organizations can identify the
opportunities that are most likely to inspire action, create customer value,
and pay off for the organization.
Creating Value from Wrapping
Customers’ willingness to pay for an offering will increase when they
believe the wrap has made the offering more valuable—that is, it is easier
and more enjoyable to find, acquire, use, store, maintain, or retire the
offering. This is the metric to watch when wrapping: has an uplift in the
value proposition of the offering been created? Uplift can be monitored
using various techniques, like tracking customer usage, A/B testing,
conducting controlled experiments, or surveys. With an enhanced value
proposition, the organization can attract new customers or motivate existing
customers to pay more, spend more, or stay longer.
The Joint Sphere
Consider that when an organization delivers a wrap, it only creates potential
value for its customers. Ultimately, it is customers who create value for
themselves by taking some action at the end of a value-creation process; the
organization gains if and when it realizes a portion of this value, for
example, by charging a higher price or by retaining the customers business
despite competition. Therefore, customer value creation must be the
primary focus of organizations that pursue the wrapping approach.
By designing a wrap to give the customer either data, insight, or action,
the organization, in effect, chooses how and how much it will help customer
value materialize. Organizations that care deeply about creating customer
value want to work closely with customers in the design and development
of wraps. The area of joint involvement in wrap design and development is
called the joint sphere (see figure 4.3).5
Figure 4.3
Organization and customer working together to generate customer value
The size of the joint sphere (the overlapping ovals in figure 4.3)
represents the extent to which the organization and customer share
knowledge (and related resources) to achieve the customers goals. Without
any joint sphere, the organization is on its own to discover how to improve
the customers value proposition. And if the joint sphere is very small, the
organization will likely be restricted to offering a data wrap. Conversely,
when the joint sphere is large, the organization is more likely to have the
opportunity to offer an action wrap.
A larger joint sphere results in better outcomes for customers and the
organization alike. Customers are more likely to create value, and
organizations are more likely to be able to realize part of that value for
themselves. Organizations grow the joint sphere by building trust and
digital connections with their customers. Customers grow the joint sphere
by sharing their data and permitting organizations to take action on their
behalf.6 In B2B contexts, moving from transactional relationships to
customer partnerships enlarges the joint sphere.
How can an organization expand the size of the joint sphere it shares with
its customers? It can begin by discovering what customers are trying to
achieve with its offerings—and how well (or not) those goals are being met.
Initially, an organization can learn this information by asking customer-
facing employees and by experimenting with data wraps that draw on the
data it already has. Next, you will see how PepsiCo grew its joint sphere
and changed the nature of its relationships with its large retailer customers.
Creating Value from Wrapping at PepsiCo
PepsiCo owns some of the world’s biggest food and beverage brands,
including Pepsi-Cola, Lay’s, Gatorade, Tropicana, and Quaker. In 2021,
PepsiCo’s products were consumed upwards of one billion times per day in
more than two hundred countries and territories, generating revenues of
US$79 billion.7
Though the company had successfully established itself as a global leader
in convenience foods and beverages in the late twentieth century, around
2010, PepsiCo saw industry growth begin to slow as markets reached
maturity, competition increased, and its core consumer group aged. Rather
than continuing to increase product variety, PepsiCo turned to data as a
source of competitive advantage.8 Specifically, the company wanted to use
data to identify pockets of potential growth and understand when and where
to place specific products in a particular retail outlet, among other goals.
In 2015, PepsiCo established a new business unit, the Demand
Accelerator, that led the development of integrative, data-driven marketing
services for its large retailer customers. The Demand Accelerator helped
PepsiCo’s IT unit build data monetization capabilities, provided enterprise-
level analytics support, and supported new kinds of retailer collaborations.
Ultimately, PepsiCo’s collaborative approach to developing wraps helped
build relationships that created win-win-win initiatives for shoppers,
retailers, and the company itself. As a result, PepsiCo earned several
industry awards recognizing its position as a top supplier.
The Demand Accelerators collaborative approach played a key role in
PepsiCo’s wrapping efforts. One early example of PepsiCo’s collaboration
with retailers involved a convenience store and gas (C&G) retail chain that
wanted to maximize its fountain drink sales, some of which were generated
by PepsiCo’s brands. However, the retailer did not have insight into drink
sales because their scan data only reflected the purchase of a cup, not its
contents.
The Demand Accelerator worked with the C&G retailer to solve this
problem. First, they assembled PepsiCo’s data about the number of gallons
of syrup the retailer used. Together they combined that with the retailers
own data about its syrup purchases from other providers. Then, using
advanced analytics, the Demand Accelerator identified specific store and
shopper attributes that influenced syrup usage. Using data wrapping
language, the Demand Accelerator created an insight wrap of analytics-
based soda syrup usage influencers—things like a shoppers age and
geography—to add value to its core offering of syrup. This insight wrap
was made possible because PepsiCo’s retailers trusted PepsiCo with their
data and also trusted PepsiCo’s analytics. Mutual trust is crucial to effective
collaboration in the joint sphere.
The retailer acted on these insights, changing how it offered fountain
drinks in a subset of its stores. The value the retailer created (for the
retailer) by this action was evident in the increase in fountain drink sales
that followed the change. Once the value of this change was clear, the
retailer introduced the new tactic more broadly. PepsiCo did not directly
charge the retailer for the Demand Accelerator services; instead, it realized
value from the wrap (for PepsiCo) in the increased volume of the retailers
syrup purchases.
Over time, the Demand Accelerator played a significant role in
supporting retailer partnerships. PepsiCo’s relationships with its retailers
were initially transactional, but those relationships evolved into
collaborative customer partnerships due largely to the Demand
Accelerators activities. In effect, PepsiCo successfully enlarged its joint
sphere with its retailers.
Realizing Value from Wrapping
It is sometimes straightforward to realize value from wrapping, as PepsiCo
did when its retail customers bought more PepsiCo syrup. The realized
value flows directly to the bottom line. As a rule, however, realizing value
from wrapping requires understanding what kind of value and how much
value customers create from your core offering because of the wrap. Based
on this understanding, combined with knowledge of the customer, product
owners can decide how to realize value: raising prices (to charge for the
wrap), selling more of the product to existing customers, selling more
complementary products, selling the product to new customers, or relying
on the wrap to retain customers who might otherwise defect, as you read in
chapter 1.
A wrap can also deliver internal efficiencies. For example, a side effect
of a good wrap might be a reduction in calls to the customer service desk.
Or an equipment wrap that preemptively schedules preventive maintenance
might reduce the need for emergency service during off-hours. To realize
value arising from the more efficient use of people or any other resource,
someone needs to remove or redirect slack, just as they do with
improvement initiatives, so that the savings can flow to the bottom line.
But there’s a complication: these more efficient resources might not
belong to the owner of the offering; they may belong to other functions and
departments. So, getting those efficiencies to the bottom line will require
cooperation from the heads of those units. Fortunately, some efficiencies
(reduced inventories, reduced warranty costs) will flow automatically to the
bottom line.
Figure 4.4
Value realization from wrapping initiatives
To summarize, as shown in figure 4.4, of the value created by the
wrapping initiative, some might be realized for the organization as reduced
costs or increased revenues—money—and some goes home with the
customer. And some value is left on the tree, so to speak. That is, some
value is created but not turned into money by the organization. Instead, that
value might increase the organization’s capacity for innovation, go home
with employees and managers, or take the form of good customer
relationships. The amount of fruit on the tree (the amount of value created)
essentially sets an upper limit on how much value the organization can
realize. In some B2B contexts, how much value the organization can realize
is subject to negotiation with the customer.9
Measuring Realized Value for Wrapping
Organizations should know what value from wrapping is hitting their
bottom line from additional sales and operating efficiencies so they can
make sure they are investing in the right things. Nonfinancial value
(employee satisfaction, customer loyalty, or extra time for innovation)
should be measured as well. Some organizations have accepted methods for
putting a value on their customers loyalty or their brand capital. For
example, at one time, Carlson Hospitality estimated that each new loyalty
program enrollment added US$20 of value to its Radisson brand.10 Ideally,
a product owner will have recorded baseline metrics that can be tracked
after the wrap is launched and going forward. This helps the product owner
understand the impact of the wrap.
In the authors’ research, product owners at a financial services company
were great at measuring value from wrapping. For example, the product
owner of one of the company’s affinity credit cards prioritized wraps related
to fraud reduction, which was a top concern for customers of that offering.
Fraud reduction wraps for this card included digital transaction statements
that contained merchant logos, geospatial maps that reduced the time and
effort it took a customer to review their purchases, and email/text alerts
when transactions suggested unusual activity, such as a tip amount that was
disproportionate to the cost of a meal.
Across the financial services company, product owners monitored how
customers reacted to new wraps by conducting experiments that compared
the attitudes and behaviors of recipients of a new wrap to control groups of
customers who did not receive it. The company’s product owners always
tied these customer reactions to changes in product sales. As a result, the
product owner of the affinity credit card mentioned earlier could easily
determine the extent to which a wrap feature increased usage of the credit
card (and thus led to revenues).
The fraud reduction wraps were so intuitive and helpful to the affinity
credit card customers that they led to a reduction in calls to the customer
service center. Although the product owner did not plan for that value, the
call center process owner gladly recognized the efficiencies and redirected
the slack to cover other call center work.
In the public sector, tracing the value of a wrap to the bottom line can be
challenging. For example, a wrapping initiative might get citizens to enroll
in a public health program and save lives, reducing public health costs but
in the long run, not the short run. At a philanthropic organization, a
wrapping initiative might show progress toward accomplishing key goals,
leading patrons to donate more to that important cause. Or a government
agency might need to wrap its offerings just to keep up with citizen
expectations of excellent service and to avoid future taxpayer revolt. In
even these cases, measurement is critical for understanding whether a wrap
is doing its job. And when measurement can be used to report on success to
key stakeholders, public sector organizations can more easily increase or
maintain their inflows from donations, budget allocation, grants, and the
like.
Data Monetization Capabilities from Wrapping
Organizations need to invest in data monetization capabilities to create
useful and engaging wraps (ones that earn high scores on the four As).
Suppose a product owner wants a wrap not just to anticipate and adapt but
also to advise and act. In that case, the organization will need more accurate
data, a faster platform, and deeper customer understanding. Its acceptable
data use capability might need to become more sophisticated, to include
oversight of the use of data by algorithms and the permissibility of acting
on the customers behalf.
Like the improving approach to data monetization, the wrapping
approach also requires all five data monetization capabilities described in
chapter 2. And, as with improving, wrapping generates greater returns from
more advanced capabilities.11
Research to consider
Product owners who say their organizations wrap more effectively than
peers reported an average return on investment (ROI) of 61 percent from
their wrapping initiatives, compared to just a 5 percent ROI by product
owners who say they wrap less effectively than peers.12
Organizations that are top performers in wrapping have better capabilities
than bottom performers (whose fans would be blank). Still, they do not
necessarily have advanced data monetization capabilities, as shown in
figure 4.5.13 Organizations can achieve valuable wrapping outcomes
without using machine learning or curated data for their wraps. Still, the
kind of wrap matters: action wraps are difficult, if not impossible, to
execute without advanced capabilities. Organizations do, however, need
baseline capabilities that facilitate customer-savvy wrap development
regardless of whether the wrap offers data, offers insight, or prompts
action:14
• Top wrapping organizations draw on rich customer data assets
regarding customer demographics, sentiments, relationships, core
offering use, and interactions with the organization, integrated to
produce a 360-degree customer view.
• By providing internal access to data and tools through a data
platform built with advanced technology, they allow employees
across the organization to access customer information.
• Their data science capabilities offer statistics skills and
understanding, allowing them to provide insights to customers as
well as to employees seeking to understand how well the
organization is meeting customer needs.
• For top wrapping organizations, sensemaking, by listening to
customers, is critical for surfacing not just core customer needs
but also latent and unmet needs.
• All organizations that wish to pursue wrapping must have some
acceptable data use capability in place to ensure that employees
use customer data in compliant and ethical ways. However, the
research indicates that this capability is difficult for organizations
to build, and even top performers are still working to get the right
new practices in place.
Figure 4.5
The capabilities of top-performing wrapping organizations
Data Monetization Capabilities at PepsiCo
The data monetization practices of PepsiCo’s Demand Accelerator unit
illustrate how much effort PepsiCo invested in capability building and how
it has accumulated the enterprise capabilities required for wrapping
initiatives. Even though PepsiCo had local data monetization capabilities
within each of its major divisions before 2015, they were siloed and often
duplicated. As a result, different divisions were serving the same retailers in
potentially inconsistent ways. The Demand Accelerator was set up to
resolve this problem by centralizing and then diffusing enterprise data
monetization capabilities across PepsiCo.
PepsiCo’s capabilities grew from adopting practices like those used by
top wrapping organizations. Back in 2012, the global IT unit had
established a data taxonomy so that PepsiCo had a “single version of the
truth” for its product sales, worldwide. The IT unit had also adopted master
data management practices and housed product sales data in a single
enterprise data warehouse. These practices built the company’s data
management capability. After the Demand Accelerator was established in
2015, its leaders worked with PepsiCo’s global technology unit to build a
data asset covering 110 million US households. This data asset was
invaluable for identifying pockets of potential growth because it included
not only consumers of PepsiCo products but most of the consumers in a
given geographic region. The data asset about consumers—referred to as
“Most Valuable Shopper” data—was integrated with data assets about
PepsiCo product sales, further strengthening the company’s data
management capability. The Most Valuable Shopper data was carefully
deidentified and structured to ensure that the data’s use would be fully
compliant with legal, regulatory, and ethical constraints. As a result, the
company’s acceptable data use capability grew stronger.
Together with IT, the Demand Accelerator established a cloud-based
platform that allowed it to source and manage more extensive and more
diverse kinds of data, drawn from both inside and outside PepsiCo. This
platform was required to store, manage, and deliver the growing collection
of data assets that fueled the Demand Accelerator initiatives. In addition,
because it was API enabled and cloud based, it allowed secure access by
retail partners, as needed. These practices contributed to the company’s
data platform capability.
The Demand Accelerator hired new expert data science talent and
upskilled their own analysts to enhance the company’s data science
capability. The data scientists created easy-to-use dashboards and reports
for marketing and sales employees across the organization. Engaging with
and learning from these marketing and sales employees helped the Demand
Accelerator better understand the needs of PepsiCo’s retail customers.
These practices helped strengthen the company’s customer understanding
capability.
Over time, people working in the Demand Accelerator packaged some of
the customer-specific wraps they had developed (wraps similar to the earlier
example of the fountain drink optimization insight wrap) into turnkey
applications for other retailers. The applications supported common retailer
use cases, like customizing the product assortment at a specific store based
on the needs of its local shopper base and successfully launching and
managing innovative marketing programs. As PepsiCo’s data monetization
capabilities became more advanced, the number of applications grew into a
suite of wraps named “pepviz” that helped retailers optimize their store
assortments and product sales.
Ownership of Wrapping Initiatives
The ideal owner for a wrapping initiative is the product owner. A person in
this role is accountable to the organization’s leaders for the overall success
of an offering that the organization delivers to its customers or constituents.
This product owner has deep knowledge of the strength and weaknesses of
the offering’s customer value proposition, how well the offering delivers on
the value proposition in the view of customers or constituents, and how well
the value proposition pays off in terms of financial returns to the
organization. For this reason, the product owner should be accountable for a
wrapping initiative regardless of whether the funding comes from the
traditional IT portfolio funding process, the marketing budget, or elsewhere.
The product owner manages a wrap just as she manages any other
product feature or experience. She needs to understand how the wrap is
supposed to impact the product’s customer value proposition. She weighs
the costs, benefits, and risks of deploying the new wrap as part of the
overall product management and development processes. She also
prioritizes the wrap opportunity along with other product-related activities
and investments.
At PepsiCo, the product owner for many Demand Accelerator initiatives
was the customer account manager accountable for the profitability of the
specific retailer or retailers who benefited from a particular wrap. So, for
example, the owner of the fountain drink wrap was accountable for the
C&G retailer account. That customer account manager was best positioned
to understand how to formulate an appealing wrap, allocate budget and
resources to make it happen, and identify what complementary adjustments
would be needed to support this customer account and this wrap initiative.
Without the account owner, the Demand Accelerator might have wasted a
lot of company resources by developing a wrap that was unrealistic,
undesired, or didn’t pay off.
An important note is that the product owner is best positioned to mitigate
the top risk associated with wrapping: value-loss risk. A wrap must meet
service levels that are acceptable to the customer. The product’s value
proposition can deteriorate if the organization deploys a wrapping initiative
that falls below the customers expectations. Imagine a wrap that displays
data that is wrong or takes several minutes to load! Product owners will
insist that wrapping delight, not disappoint, their customers.
The product owner plays a vital role for data wrapping initiatives, but as
with improving, wrapping requires getting all kinds of people on board to
ensure success. Yes, wrapping, too, takes a village. Think of all the people
who “touched” the fountain drink wrap at PepsiCo: data scientists and
marketing specialists in the Demand Accelerator; data, systems, and
technology people who worked with the data assets used for the initiative;
sales teams; and people in supply chain and distribution who were needed
to fulfill requests for more products. Indeed wrapping, like improving, is
everybody’s business.
Time to Reflect
Each touchpoint along the customer journey—at purchase, during use, or
while engaging with customer service—offers different wrapping
possibilities. And these possibilities might entail delivering data, insight, or
action to your customers. Regardless of which wrap type makes sense for
your organization and your offerings, you need to have a clear picture in
mind of the customers offering-related goals, how the wrap generates
customer value, and how your organization can realize value. Here are the
key points from this chapter to keep in mind:
• Wraps are enhancements to physical or intangible goods or
services you offer to your customers. Which of your offerings
seems most likely to have a value proposition that could be
enhanced with data or analytics?
• Wraps can deliver data, insight, or action to your customers; action
wraps require that you deeply understand your customers’ value-
creation process. Where in your organization would you find that
kind of knowledge of customers? Would you need to partner with
customers to understand how they create value from your
offerings and how that value could be increased?
• Wraps differ in the extent to which they anticipate, adapt, advise,
or act on behalf of customers. Consider any wraps you already
have for your offerings—how well do these wraps anticipate,
adapt, advise, or act?
• Wraps can create value for customers, and an organization can
realize some of that value. How do you realize value from your
existing wraps? Do you have existing metrics or approaches to
measure the value customers realize from your core offerings that
you could exploit to assess customer value creation or realization
from a wrap?
• Wrapping initiatives draw on all five capabilities, not just customer
understanding. Looking back, can you think of a wrapping
initiative that failed because the needed capabilities were
unavailable? What capability-fostering practices did that
initiative require?
• Wrap initiatives should be owned by the owner of the offering that
will be enhanced. What will it take to engage owners of your
offerings in a wrap initiative?
In the process of wrapping some of your goods or services, it would not
be unusual to get to know your customers well enough to see an opportunity
to sell a customer (or a different customer) a completely new solution based
solely on your organization’s data assets. That’s the subject of the next
chapter.
5
Selling Information Solutions
First, you need to understand what your customer is challenged with. Then consider:
what do you have that can be brought to the table to provide a solution and that can
scale and grow in a changing environment?
—Don Stoller, Healthcare IQ
You’ve made it to the third approach to data monetization: selling
information solutions. Before you picked up this book, there is a good
chance you thought “data monetization” was just another name for selling
data sets. Now you know that data monetization is much more than selling
—it’s improving and wrapping too! In this chapter, you will discover that
selling involves more than selling data—it’s selling insights and action too!
Your organization can package and sell a variety of standalone
informational solutions that help customers solve important problems.
Historically, organizations pursued selling when they recognized that
they had data for which other organizations would pay handsomely. For
example, in the medical supply distribution space, back in the 1990s,
Owens and Minor (OM) accumulated loads of medical supply cost data as it
distributed medical supplies from thousands of manufacturers to hundreds
of hospitals. In 2004, it established a business called OM Solutions that
converted OM’s vast trove of cost data into data assets that that could be
used to create hospital spend analytics solutions. The hospitals used the OM
Solutions offerings to manage their medical supply costs more effectively
and save money.1 In the retail grocery space, Kroger established its 84.51°
business to leverage POS data assets that were delivered to retailers via
marketing analytics tools and advisory services. The retailers used the tools
and services to create more personalized experiences for shoppers across
their purchase journey and to make money.2
After learning about the challenges of the improving and wrapping
approaches, it might seem simpler just to sell your organization’s data to
generate big returns. Warning: it’s a rewarding but higher-risk option. The
selling approach to data monetization involves standalone information
solutions; there is no underlying core offering with a value proposition that
is merely enhanced. Selling organizations must create information solutions
with their own appealing value propositions for which customers would
pay. They must create solutions that meet current market needs and then
adapt and expand the solutions to continue to profit while serving new and
changing market needs. And they must do this while fending off scrappy
competitors eager to enter a potentially high-margin business.
Questions to ask yourself
As you read this chapter, imagine new opportunities for your
organization. Are there problems that customers would pay you to solve?
Can you fill that need by selling an information solution? Do you have
data assets that could be used to inform such a solution?
Research to consider
Survey respondents, on average, reported that selling accounted for 18
percent of their revenues from data monetization activities, making it the
least prevalent of the three approaches in the improve-wrap-sell
framework.3 No doubt, this reflects the complexities of selling
information solutions.
Types of Information Solutions
As data proliferates, more organizations see opportunities to solve others’
problems with their data assets. For example, a medical device
manufacturer—with sensor data assets that reflected patient health—saw
that it could help clinicians offer better diagnosis and care. A custodian
bank—with actual cash flow data assets associated with private equity
funds—saw that it could help investors evaluate and analyze private capital
markets. And in chapter 2, you read that BBVA created a deidentified bank
card data asset that it offered via its BBVA D&A subsidiary. Over the years,
BBVA learned that its asset could help urban planners understand the
economic impact of city decision-making, disaster recovery managers
prioritize relief efforts, and merchants better target and attract customers. In
all of these cases, the organizations expected to leverage their data assets to
sell any of the following: raw data, prepared data, reports, analytics, or
analytics-based consulting services.
Selling might seem to be a good fit for organizations with deep domain
expertise and an engaged customer base.4 However, organizations with an
incumbent business model tend to impose the practices and values of their
existing business on their selling business. This invariably creates high
overhead costs, unnecessary regulatory constraints, bureaucratic processes,
rigid data use terms, or conservative talent management practices, any of
which can hurt the viability and profitability of an information offering.
Organizations need to allow their information businesses to pursue a unique
business model without interference. Notably, OM and Kroger both
established separate business units to build and nurture their respective cost
management and marketing insights businesses. Both companies
recognized that selling was different than their respective distribution and
retail businesses, and the separate units ensured that information solutions
received dedicated managerial attention and resources.
All information solutions fall into one of the same three basic types that
improvements and wraps did: they offer data (data solutions), insight
(insight solutions), or action (action solutions).5 As with wraps, information
solutions create value only in theory until the value-creation process is
completed, usually by the customer. As illustrated in figure 5.1, sellers, in
addition to having little control over the customer and perhaps knowing too
little about the customer, can be far from the action and value creation. That
distance can make it hard for selling organizations to price solutions
correctly or understand how to evolve and shape the solutions over time.
Figure 5.1
How information solutions differ in their scope
Information Solutions That Offer Data
Although all kinds of data sets are increasingly available at no cost via
“open data” websites and public sector initiatives, selling data is still a huge
industry. The global data broker market, which includes companies that
collect and sell users’ internet information, was valued at US$232 billion in
2019.6 In 2022, Verisk, Inc., a firm that serves the insurance and energy
industries, had 19 petabytes of information in its data stores, an insurance
fraud database with more than 1.5 billion claims, and models covering
natural hazards in more than one hundred countries.7 More broadly, the
industry includes organizations that specialize in developing unique
proprietary data assets by combining multiple data sources, ingesting data
from rare sources, generating data based on a platform business or across an
ecosystem, or collecting contributory data from a set of peer organizations
(often competitors!).8
Organizations that focus on data solutions work to create data assets that
customers can easily plug into their own data environments. Data assets that
customers purchase usually fill gaps in their own data assets, allowing the
customer to do analyses or take action that they otherwise could not do. The
cheaper and easier it is for customers to access and use the data assets, the
more appealing the assets will be to the customers, and thus the more
customers will pay for them. For this reason, raw data (data that is
minimally processed) rarely will command as high a price as data that has
been cleansed, standardized, validated, enhanced, and teed up for analysis.
TRIPBAM, a privately held company in Texas, was founded in 2012 to
help travelers reduce their hotel expenses.9 Over time, it evolved to focus
on the corporate travel market, helping organizations’ travel units manage
travel costs and contract compliance. In 2020, TRIPBAM served the travel
needs of roughly half of all Fortune 100 companies. The company offered
all three types of information solutions: data, insight, and action. Let’s look
first at its data offerings.
TRIPBAM developed a portfolio of reports (data solutions) that featured
information of importance to travel buyers, such as negotiated room rates,
amenities adjustments, and corporate program adherence. Large customers
paid TRIPBAM a monthly subscription fee to access the reports and other
services. During the Covid crisis, TRIPBAM leveraged its visibility into
hotel rates to develop novel reports specific to the pandemic. For example,
the company launched a weekly closure report that helped its customers
know whether a hotel was operating or had shut down (another data
solution). TRIPBAM published the closure report for free (such information
was of great interest to the industry and policymakers) to demonstrate the
value that could be extracted from its data assets.
Information Solutions That Offer Insight
As a consumer, you likely are familiar with the consumer credit score, an
insight solution that reveals the likelihood that a person will default on a
debt. Companies that provide credit scoring have developed backend
calculations that are proprietary and quite sophisticated. The scores appeal
to consumers as well as organizations that offer loans or leases, credit cards,
and home mortgages. Employers use them too.
Information solutions that offer insight use analytics to help customers
make better decisions. Scores, benchmarks, alerts, and visualizations help
customers view and understand data in ways that are tailored to their
specific context, helping them prevent or solve problems. However,
customers must use the insights—take some action—to find value in them.
Therefore, organizations maximize the value potential of their insight
offerings by delivering relevant and understandable insights, which fit
naturally into their customers’ workflows.
When TRIPBAM first entered the hotel rate shopping industry in 2012, it
pioneered “clustered rate monitoring” in the hotel industry, which involved
monitoring three metrics for a cluster of hotels within a given area: rate
fluctuations, the best available rate, and the last-room-available rate. These
metrics were the basis for several of its insight solutions. One such solution
was to find better hotel rates and suggest rebooking opportunities.
Rebooking opportunities were insights that could lower travelers’ hotel
costs if they rebooked their hotel rooms. Later, when TRIPBAM began
serving corporate travel buyers, the company sold insights that informed
corporate travel buyers about instances of noncompliance with their hotel
rate agreements. But travel buyers still had to follow up with the hotels and
attempt to extract refunds. In both cases, the value-creation potential of the
insight was quite clear, but customers did not always follow through—take
action—and realize savings.
Information Solutions That Prompt or Trigger Action
Ideally, a seller offers information solutions that trigger action by executing
a task or by doing something on the customers behalf. Task automation,
process automation, and process outsourcing are ways for sellers to take
action on a customers behalf. These kinds of solutions involve the
organization most deeply in customer value creation. In some cases,
information solutions simply prompt customers to act on insights by
making it very easy or very valuable to do so. Consulting and on-site
support are also action-prompting offerings that leverage the organization’s
accumulated expertise to prompt customer action. The appeal of consulting
and on-site support is that both give an organization a front seat view of
customer value creation.
Over time, TRIPBAM learned to automate action taking in its solutions
when it was possible. For individual travelers, TRIPBAM automatically
rebooked hotel stays when a better deal was identified that matched the
travelers preferences and constraints. For travel buyers, when incidences of
contract noncompliance were identified, the company generated automatic
emails to hotels warning them that they would be removed from the buyers
travel program unless they complied with specific contractual obligations.
Facilitating customer action ensured that TRIPBAM’s information solutions
created value for its customers, and it also made the company’s solutions
“sticky” because they became embedded in customer habits and processes.
Competitors were less appealing to travel buyers who came to consider
TRIPBAM reports and services as part of their own standard operating
procedures.
For organizations that have advanced data monetization capabilities,
automation is achievable. Automation eliminates customer effort in taking
action, guaranteeing that the customer will obtain value from the solution.
Before customers will accept automated action taking, however, they must
have deep trust in the providers intention and ability. Therefore sellers need
to communicate clear rules of engagement that govern automated actions,
and they must establish transparent action taking that they can explain and
monitor.
When TRIPBAM automated the process of securing the best-value hotel
accommodation for travelers for its corporate customers, it was quite a feat.
The service had to cancel an existing reservation and move the traveler to a
new comparable room at a lower rate while complying with corporate
agreements, satisfying traveler preferences, and abiding by hotel
cancellation policies. The service resulted from years spent gaining
experience with and knowledge about the overall rebooking process,
building credibility with its travelers and travel buyers, and investing in
technologies that could support fast, secure, and reliable rebooking
transactions.
Selling Information Solutions at Healthcare IQ
TRIPBAM’s range of data, insight, and action offerings—and its shift to
offering action solutions over time—is typical of selling organizations.
Let’s look at one more seller that experienced a similar journey, this one in
the healthcare field.
Healthcare IQ is a privately held healthcare spend management company
based in Florida.10 It was founded to help hospitals manage their data about
patient billing. Initially, this meant helping hospitals collect, clean, and
standardize their data about patient medical procedures and the associated
medical supplies used for those procedures. This was difficult because
patient billing data was managed in many different systems, often in
inconsistent formats. A hospital could have a single syringe in their systems
that looked like twenty distinct products because it was recorded in twenty
different ways; Healthcare IQ knew all those ways and could map them to
one correct item. Over time, it grew its ability to fix data anomalies like this
(using its proprietary product master catalog). The company also amassed a
trove of hospital expenditure data and a deep understanding of hospital
spend management problems.
Around 2000, the US federal government began to pressure hospitals to
manage their costs. By that time, Healthcare IQ’s leadership was confident
that the company had built up the data monetization capabilities required to
compete in the emerging healthcare spend management industry. In
addition, leaders believed that the data assets the company had meticulously
gathered and curated over the preceding decade (specifically its hospital
expenditure data and its product data catalog) could be used to create
information solutions that would help hospitals drive down costs.
Healthcare IQ’s portfolio of offerings changed over time, beginning with
a focus on data solutions and moving to solutions that prompt action.
Data solutions: In its first decade of operation, Healthcare IQ
helped hospitals clean and standardize their patient billing data.
Over time, it built a team of clinicians who developed tools and
processes to enrich its product catalog. The company created new
fields that were helpful to hospitals, such as a product
equivalency field that indicated which products could be
interchanged without concern.
Insight solutions: Healthcare IQ’s offerings evolved to include a
web-based reporting interface that helped hospitals benchmark
their spending against the spending of other hospitals and medical
entities. Over time, Healthcare IQ incorporated interface
visualizations, alerting, and exception reporting to better highlight
for customers precisely what they should be learning from the
reports. In 2011, Healthcare IQ rolled out Colours IQ, a Google
Maps–like experience based on a proprietary tool that delivered
data visualizations via hundreds of thousands of predefined pivot
tables. Colours IQ helped users identify and evaluate potential
savings opportunities using visual features, such as colors, to
indicate spend levels that were above or below expectations.
Action-prompting solutions: By 2014, Healthcare IQ offered on-
site consulting services to help customers take appropriate actions
based on the insights in its reporting tools. Consultants were
placed with hospital teams to help the teams access, interpret, and
act on savings opportunities. To convince hospital leaders to buy
its consulting service, Healthcare IQ offered a shared-savings
model, earning revenues based on how much it could help the
hospital save.
Creating Value from Selling
Like wrapping, selling creates value at the hands of the customer after the
customer acts on the sellers data, insight, or action solutions. But this does
not mean that selling organizations passively wait around for that to happen
—quite the opposite. Experienced sellers know how the customer value-
creation process should unfold, including how the information solution will
get used. These sellers also come to expect that a customer will sometimes
drop the ball. Sellers constantly analyze customer behaviors, sentiments,
and needs, proactively monitoring data or insight access, tool usage, or
action taking. When monitoring is proactive, the seller will have time to fix
any failure to act using education, product design, customer service, or
incentives. As with wrapping organizations, selling organizations often
choose to offer solutions further down the data-insight-action value process.
Because selling often involves seizing new market opportunities, new
customers are usually in the mix. It follows that this new customers value-
creation process and desired customer experience will take some time for
the seller to appreciate. Here, too, collaborative development can ensure
value creation. As with wraps, information solutions benefit from
development approaches that leverage collaborative customer relationships.
They allow the seller to learn what value the customer is creating and how.
In the case of TRIPBAM, company leaders focused on delivering a
compelling ROI to their customers. To monitor this, they computed a client-
specific ROI by tracking each customers savings compared to how much
the client paid TRIPBAM for services. Not surprisingly, TRIPBAM had
virtually no customer churn.
Realizing Value from Selling
Information solutions, like wraps, are typically priced starting with a careful
analysis of how much value is created for the customer. The solution cannot
be priced beyond its potential value to the customer, at least not for long.
For example, Healthcare IQ expected a US$100 million hospital to achieve
at least US$2–3 million in cost savings; TRIPBAM tried to deliver 2–3
percent in overall savings on its customers’ total travel spend (which could
be as much as US$10 million). With knowledge of potential customer value
in hand, organizations can choose a pricing strategy that makes sense for
their specific information solutions and will work for the customers buying
them.
Data solutions are often viewed and priced as commodities. If there are
only a few customers for a data solution, one way to price the data is at
auction. For example, an investment bank that wants to predict market
trends more accurately might be willing to pay top dollar on auction for
exclusive rights to a rare data asset. Those buying data at auction probably
know precisely what it is worth to them. In the case of investment banks, it
could be millions of dollars.
Pricing information solutions that offer insight or that trigger action is
challenging. Still, cocreating with a few customers, monitoring use, or
providing consulting services to them can afford an insider view of the
customer experience. Initially, selling organizations might establish a shared
value agreement whereby they take on the costs and risk of developing an
information solution for a customer in return for some percentage of value
that materializes for the customer. In these cases, sellers often develop
offerings that prompt or trigger action to ensure value creation, the tree of
fruit to be shared. TRIPBAM offered value-sharing deals to its smaller
clients that could not reliably predict the number of monitored reservations
they would need and didn’t want to overpay for TRIPBAM’s monthly
subscription services. The gainsharing model entitled TRIPBAM to receive
25 percent of the realized savings for any reservation that it rebooked on the
smaller client’s behalf.
Figure 5.2
Value realization from selling initiatives
As with wrapping, information solutions create value, some of which is
realized for the organization in the form of money and some of which goes
home with the customer whose problems are solved, as shown in figure 5.2.
And, as always, some value is left on the tree, so to speak; some value is
created but not turned into money by the organization. In the case of
information solutions, that value might take the form of brand capital, good
customer relationships, or innovative capacity. And, as noted earlier, the
amount of value that can be realized cannot exceed the amount of value
created. You can only pick as much fruit as is on the tree.
Another consideration when pricing information solutions is the position
of the information solution in a competitive marketplace. What a customer
is willing to pay depends on both how much potential value the solution
promises (assuming the value-creation process will happen) and the price of
alternative solutions. (If the solution is competitively distinct, alternatives
don’t matter, but how much value the customer expects to create does.) An
information solution—like any type of offering—is competitively distinct
when it is rare, cannot be imitated, and can withstand the threat of
substitutes.11 For example, when Healthcare IQ’s offerings were first on the
market, they were one of a kind, so they were definitely rare.
A competitively distinct solution is difficult to imitate. Often, its
mechanics are complex, hidden, or protected from imitation by patents.
Healthcare IQ worked with a technology partner to develop a unique
visualization offering that their hospital customers loved. It was easy to
understand and use, but it was not easy to reverse engineer or recreate from
scratch. Healthcare IQ’s CEO believed so strongly in the underlying
technology, called fractal maps, that he purchased the fractal map company
so that Healthcare IQ would hold the fifty-plus patents associated with the
technology. The CEO wanted to prevent competitors from partnering with
that same tech company to create similar solutions.
A competitively distinct solution can withstand the threat of substitutes.
It’s hard for customers to find a comparable replacement. An organization
can do this by developing features and benefits that customers can’t find
elsewhere. This is a key reason why sellers also do a lot of wrapping! They
rely on wrapping to boost a solution’s customer value proposition again and
again.
In fact, the biggest threat to information solutions is the copycat provider
—the cheaper substitute. Because of this risk, an organization not only
needs to offer a unique and desirable information solution but also must
maintain the solution’s uniqueness and desirability over time. Otherwise,
revenue streams will dry up. Years ago, when Magid Abraham, then CEO
of Comscore, was speaking to a class about selling information solutions,
he said, quite passionately, “Information products are obsolete upon
launch!”12 The information solution marketplace can be ruthlessly
competitive. Competitive pressures force information businesses to
continuously innovate and improve their information solutions so they can
keep their solutions distinct from those of competitors.
So, how do information businesses create solutions that maintain their
competitive advantage over time? They draw on the following sources of
value that allow them to create offerings that are rare, difficult to replicate,
and difficult to substitute:13
• Unique data that is sourced, combined, or enhanced to produce
one-of-a-kind data assets.
• Cost-effective, proprietary platforms that can process data and do
things that competitors simply can’t do or can’t do as cheaply.
Proprietary platforms are notoriously difficult to reverse engineer.
• Sophisticated data science and data scientists who are passionate
about solving problems with data. While one algorithm might be
replicable or substitutable, a complex combination of algorithms
that is the brainchild of sophisticated data scientists is far less
likely to be.
• Domain expertise that sellers promote by having their domain
experts speak at conferences, sit on standards boards, and publish
industry white papers and academic articles.
• Customer empathy that helps sellers understand and appreciate
customer problems deeply. It also helps them identify ways to
monitor and measure their ability to create customer value.
Note that these sources of value are rooted in data monetization
capabilities. This is yet another reason organizations engaged in selling
initiatives need advanced capabilities.
Capability Considerations for Selling
While Healthcare IQ developed a successful business model around selling,
selling is still inherently higher risk than improving or wrapping. Selling
organizations face the need to develop and grow new markets, the need to
establish a new business model, the need to keep up with data privacy laws,
and the need to constantly fend off competitive threats. To overcome these
challenges, organizations that sell information solutions rely heavily on
advanced enterprise data monetization capabilities in all five areas, as can
be seen in figure 5.3.14
Organizations that are top performers in selling information solutions
report having the following data monetization capabilities:
• Unique, high-quality data assets that are easily combinable,
including with their customers’ data.
• Advanced technology data platforms that offer secure, fast, and
reliable access to both internal and external users.
• The ability to use statistics to extract sophisticated insights from
vast quantities of data.
• The ability to experiment with solutions to uncover customer
needs and wants so that new market needs are constantly served
with appealing offerings with a high likelihood of creating
customer value.
• Automated data use controls that ensure the protection and
oversight of sensitive and valuable information at scale.
Figure 5.3
The capabilities of top-performing selling organizations
The final point warrants further attention. Modern life has become highly
quantifiable and connected. As a result, data assets that are used to develop
information solutions can contain sensitive data. They can reflect the
behaviors of individuals as customers, citizens, employees, students, and
activists. As of this writing, too many organizations lack adequate local,
federal, and global safeguards associated with data asset sourcing,
manipulation, use, and protection. Having an advanced acceptable data use
capability is critical to navigating ethical challenges.15 An organization
must have an enterprise ability to make sure that data asset use is not only
compliant with regulations but also consistent with the values of its
stakeholders. In fact, when considering ethics, companies might want to be
more restrictive with their data than current regulations stipulate.
Regrettably, a deeper treatment of this complex topic is outside the scope of
this book.
As noted earlier, more advanced capabilities are associated with better
data monetization outcomes. In the case of selling initiatives, however,
advanced capabilities are a requirement rather than an option.
Data Monetization Capabilities at Healthcare IQ
When people look under the hood of an information business, they often are
surprised by the sophistication and innovation of the data monetization
practices they see. Such practices are often a necessity. A selling
organization might have a hard time finding commercial technology
powerful enough to process its massive amount of data, so it builds its own
hardware and software. A selling organization might need to establish
credibility before entering a new market with a solution, so it hires the most
highly regarded data scientist in the field. A selling organization might need
to reassure its investors of the security of its sensitive data assets, so it
establishes data oversight methodologies that go well beyond regulatory
requirements. Regardless of whether a seller needed to adopt innovative and
sophisticated practices or simply thought it wise to do so, it’s fair to say that
advanced data monetization capabilities are part of the game at well-
established selling organizations.
This was certainly the case at Healthcare IQ. Over time, by need and by
choice, the company adopted increasingly advanced ways to manage,
distribute, and oversee its data assets and effectively serve customers.
Leaders relied on technologists, systems integrators, content specialists,
sales account managers, and customer service providers to propose helpful
practices—or identify needs for practices—and incorporate them into
operations.
Healthcare IQ established a data management capability as it developed
a way to standardize, match, and validate data ingested from its hospital
customers’ transaction systems. At first, data problems were fixed
manually. Then as the team gradually identified the root causes of
problems, it established business rules and automated the fixing using
custom workflow software, resulting in increasingly cleaner data assets
over time. The company also developed tools and processes to enrich the
data. Enrichment activities ranged from mapping products to the correct
manufacturer, reviewing and tagging products for equivalency, and
classifying products so that analysts at Healthcare IQ’s hospital customers
could develop better reports.
At the heart of Healthcare IQ’s data platform capability was a proprietary
custom-built data warehouse that was managed by technologists with skills
in data architecture, virtualization, database development, infrastructure,
open source, and software engineering. The technologists learned how to
serve the company’s internal data processing and distribution needs as well
as the needs of hospital customers. For the latter, they built out faster and
more efficient ways for the hospitals’ IT people to submit hospital data; for
example, they developed a simple interface that hospitals could use to
check whether their data files met Healthcare IQ loading specifications.
This avoided subsequent problems that might have occurred from bad,
mislabeled, or missing data fields.
Healthcare IQ was ingesting data from multiple hospital customers, so it
began to develop data assets based on this aggregated data (after securing
permission to do so). Data analysts used the assets to calculate benchmarks
and indices and to create reports to solve a hospital’s cost management
problems. As mentioned earlier, in 2011, Healthcare IQ introduced Colours
IQ, an advanced analytics tool that delivered data visualizations via
hundreds of thousands of predefined pivot tables. The CEO viewed the tool
as an essential contribution to the company’s data science capability.
Several years later, Healthcare IQ hired an AI expert to explore ways for the
company to benefit from machine learning.
Healthcare IQ sales and service teams were instrumental in building the
company’s customer understanding capability. The team members
interacted with customers during weekly phone calls, informal
conversations, and emails; cocreated novel offerings with customers during
consulting engagements; and learned about customer needs during quarterly
business reviews, on-site training, and routine support. For example, during
support, a customer might ask for a particular attribute to be added to a
report. Team members mined support experiences to identify ways to add
functionality to existing products, develop new offerings, or automate
customer processes, submitting ideas to a system that tracked them.
Management discussed and prioritized submitted ideas at a weekly staff
meeting, and ultimately, high-priority ideas flowed into product
development. To get deeper insight into customers, Healthcare IQ hired
people who previously worked in customer or partner organizations when it
could.
Finally, Healthcare IQ developed its acceptable data use capability to
ensure that hospitals were comfortable with the company’s guardianship of
their data. Initially, Healthcare IQ established HIPAA-compliant processes,
policies, and procedures. Later, leaders pursued HITRUST certification,
which would corroborate Healthcare IQ’s conformance with security best
practices. The company sought external validation of its efforts, and it
promoted such confirmation to customers, partners, and other stakeholders.
Healthcare IQ’s advanced data monetization capabilities positioned the
company to cope with the turbulent dynamics of the healthcare spend
market. Hospitals that had historically focused on understanding their costs
were prompted by new US government regulation to understand their costs
within the context of clinical outcomes. New competitors—including
software providers, consultants, distributors, industry associations, and
start-ups—began to offer spend analysis solutions to their hospital
customers. Customer expectations grew as hospitals developed savvier
talent and modernized their systems. Despite such strong forces, Healthcare
IQ drew on its capabilities to adapt accordingly and stay competitive.
Ownership of Selling Initiatives
Like wrapping initiatives, selling initiatives should also be led by a product
owner. However, in the case of selling, the “product” is an information
solution. (Remember, this book uses the term information solution owner to
distinguish between wrapping and selling owners.) Information solution
owners manage an informational product with its own value proposition,
whereas product owners use wraps to enhance their core product’s value
proposition.
An information solution owner is accountable for the overall profitability
of the revenue streams associated with selling. Because of the specialized
expertise needed for this type of role, organizations often recruit seasoned
professionals from information businesses, technology companies, or
successful digital-native companies to be information solution owners.
These people bring a solid customer-centric mindset and experience in
planning, developing, and delivering information offerings to the role. The
information solution owner manages the costs, risks, and benefits associated
with the information solution.
You can think of an information solution owner as the mini-CEO of an
information solution, coordinating the disparate activities required to
produce, market, and sustain it, including solution design, compliance,
sales, marketing, and IT services. Like a CEO, the owner of an information
solution depends on expertise and commitment from across the enterprise.
In an information business or a business unit devoted to information
solutions, virtually every employee is involved in some aspect of the
information solutions: design, compliance, sales, marketing, after-sales
service, and, last but not least, IT services. So it should not surprise you that
selling organizations employ people at all levels who deeply understand the
customer problem domain—whether it be hospital health costs or hotel
travel spend—and passionately want to help customers solve problems
using data. As a result, selling, too, is everybody’s business!
Time to Reflect
In theory, every organization that has data can use it to create data assets
that can give rise to information solutions. If your organization wants to
pursue the selling approach, start with important problems that someone,
somewhere, will pay your organization to solve. Here are the key points
from this chapter to keep in mind:
• Instead of thinking about ways to sell your vast troves of data,
think about what customer problems could be solved using your
data assets. Who in your organization knows the most about the
important problems customers are struggling to solve?
• Once you’ve identified a customer problem that your organization
could solve as long as the customer took some specific action,
you will need to work to ensure that the customer actually takes
that action and realizes value from it. Which of your customers
might work closely with someone in your organization on this?
• It’s essential that your organization’s information solutions be
competitively distinctive. What distinctive assets does your
organization have that would make your information solution
rare, hard to imitate, or difficult to do without?
• Organizations that are adept at selling have high levels of data
monetization capabilities. Where is your organization in terms of
accumulating these capabilities? Which capabilities need to be
addressed first? What would be the best way to go about building
these capabilities?
• To offer an information solution, you must establish a supporting
business model. Where in your organization would you find the
new market development, product strategy, and other kinds of
expertise your information business would need?
You should now have a good handle on the range of data monetization
initiatives that can make money for your organization— improving,
wrapping, and selling—and what it takes to pull them off. In the next
chapter, you will learn about the ideal organization context for data
monetization: a data democracy.
6
Creating a Data Democracy
The more we increase access to data, the more we enable curiosity and innovation.
—Rob Samuel, CVS Health
At BBVA, Microsoft, PepsiCo, Healthcare IQ, and all the organizations you
have read about so far, people of all kinds are inspired to engage in data
monetization. They are rewarded for questioning the status quo, sharing
ideas, adopting novel practices, changing habits, and contributing to
organizational goals. They believe that data is valuable, is essential, and
plays a role in the organization’s success. This kind of organization, so
conducive to making money from data, is called a data democracy.
It takes a lot of effort to get the average employee ready and willing to
participate in the data monetization movement. Part of the challenge is
rooted in the old problem of data versus domain knowledge. Domain
experts (accountants, marketers, nurses, civil servants, factory workers,
sales associates—anyone with expertise in a part of an organization) and
data experts (analysts, data scientists, dashboard designers, database
administrators) each have something important to offer to an improving,
wrapping, or selling initiative. For example, to fix a process glitch, you
need a process manager to interpret the problem and a software developer
to write the code. But before coding can start, the developer must
understand the problem and the process manager must recognize the
potential of data assets and data monetization capabilities. It’s tricky to
come to a common problem understanding, using the same language, and to
agree on the optimal use of these data monetization resources. Turf battles,
skill gaps, and politics get in the way. Nevertheless, leaders of data
democracies actively manage through these hurdles and design their
organizations for success.
Data monetization resources are the full set of resources that speed up
data monetization initiatives, including data assets and data
monetization capabilities. Data monetization capabilities may be found
in people with expertise or expertise embedded in tools, routines,
policies, forms, software, and so on.
In short, your organization won’t become a data democracy organically.
Data and domain experts must be motivated to learn from each other.
Without a deep understanding of the organization’s needs, data experts will
be hard pressed to develop the most useful data monetization capabilities
and the most reusable data assets. Shared knowledge—more data savvy
among domain experts and more domain savvy among data experts—is the
key to valuable innovation as well as the diffusion of those innovations—
scaling and reusing them. Innovation and the diffusion of innovation are
achievable in data democracies. This chapter describes the specific
organizational design elements that underpin a successful and sustainable
data democracy: data-domain connections and data democracy incentives.1
A data democracy is an organization with pervasive employee
appreciation of, access to, and use of an organization’s reusable data
assets and data monetization capabilities (i.e., its data monetization
resources).2
A question to ask yourself
What keeps your domain and data experts from collaborating to leverage
your organization’s data monetization resources?
Data-Domain Connections
Imagine that all the “data” people in an organization were colored red and
all the “domain” people were colored blue. As these red and blue people
regularly interact, share what they know, and learn from each other, their
knowledge blends and they become less red or blue and more purple. They
develop a shared grasp of data in their particular organizational context. A
data democracy is populated by purple people!3
Organizational design is commonly thought of as the way in which
workflows, authority relationships, and social ties are organized within the
organization. In the case of data democracy, workflows, authority
relationships, and social ties are configured into structures that blend red
and blue people. The blending occurs by virtue of data-domain
connections: organizational structures linking data experts and domain
experts that facilitate knowledge exchange and learning.
Data-domain connections are structures that facilitate knowledge
exchange between data experts and domain experts.
Dr. Ida A. Someh, a long-time collaborator of the book’s author team,
studied how relationships between analytics groups and business-domain
groups can be configured to facilitate knowledge integration in data-driven
organizational initiatives. She found five common data-domain
connections: embedded experts, multidisciplinary teams, shared services,
social networks, and advisory services. (See figure 6.1.) These five
connections are different means of knowledge sharing—creating purple
people—crucial to both innovation and the diffusion of innovations across
the organization. They work differently, and they work together. Think of
these connections as tools in your organizational design toolkit, the data
democracy special edition toolkit. Organizations can use any and all of the
five connecting structures; ideally, organizations should support enough
structures to yield as much of a data democracy as they need.
Figure 6.1
Five data-domain connections that facilitate knowledge exchange and learning
The connections facilitate two-way collaboration, conversations, and
learning. They build on and help consolidate any knowledge gained in
formal training experiences. For example, if a domain expert takes a
statistics course, a data expert can help to apply that new skill to a particular
problem. If a data expert takes a course in marketing, a domain expert from
marketing can help contextualize concepts from the course to the specific
organization. The connections make it easier for domain experts to become
aware of, access, and use data assets and data monetization capabilities as
they engage in improving, wrapping, and selling initiatives. The
connections also make it easier for data experts to understand how to make
data monetization resources more valuable to the organization. The more
knowledge transfer is activated, the more organizations can fully develop
and exploit superior data monetization resources. When data democracy is
advancing too slowly and data monetization assets and capabilities are
stuck in silos, organizational leaders might find it beneficial to introduce a
few additional connecting structures. Let’s step through each one in turn.
Innovation Connections
Two of the connections link data and domain people in ways that foster
innovation: embedded experts and multidisciplinary teams. (See the top of
figure 6.1.) With innovation connections, data and domain people exchange
knowledge and generate new and improved tasks and processes and new
and enhanced products and solutions.
For example, when an organization embeds a data expert full time in a
marketing department, it is easier for marketing employees to find new
ways to exploit existing data assets in their daily work. Thus, the
organizational capacity to envision and undertake big new initiatives gets
stronger. Maybe the data expert knows how to use an algorithm to identify a
“next-best offer” (the most relevant thing to offer a particular customer at
that moment). The data expert would help the marketers experiment with
using this algorithm to target offerings to different customer segments. The
result will be new knowledge on both sides of the connection: the data
person will know more about this marketing situation, and the marketing
people will know more about next-best offer algorithms. They will both be
a little more purple. With a better understanding on both sides, the
marketing department (including the embedded data expert) might begin
testing the effectiveness of AI-suggested next-best offers. The test results
might inspire a new improving initiative to make the process of selecting
next-best offers faster and more precise, potentially reducing some costs
and increasing sales. This is how embedded experts foster innovation.
Similarly, when an organization assembles a multidisciplinary team to
carry out some initiative, perhaps one to solve a customer churn problem,
they are ensuring that both data and domain perspectives (a variety of
domain perspectives, probably) will inform the solution. Picture this:
marketers in an organization are stuck in a rut and are using outdated tools
for managing customer churn. A multidisciplinary team is formed, charged
with proposing a machine learning approach for customer churn
management. The data scientists would share contemporary ways to predict
customer churn based on both internal and external data. Sales domain
people would explain how salespeople currently connect with customers to
keep them satisfied. Marketers would contribute timeless customer
retention principles. As the team members share and absorb new
knowledge, they formulate ideas about managing customer churn and
propose an improving initiative. Notably, not only does the
multidisciplinary team develop a meaningful data monetization initiative
that solves the customer churn problem, but the people on the team have
each become more purple. These purple people are more capable of
accessing, contextualizing, and using data related to customer churn and
machine learning to create new innovations.
Diffusion of Innovation Connections
If an organization only draws on embedded data experts and
multidisciplinary teams, then over time, localized silos of innovation will
proliferate. This is why connections that promote the diffusion of
innovations—shared services and social networks—are essential. (See the
bottom of figure 6.1.) These connections help spread innovations of all
shapes and sizes to other parts of the organization. Diffusion occurs when
people reuse, rather than reinvent, innovations that exploit data assets and
capabilities. The reuse of process improvements across similar
organizational contexts is one way to increase the value created and realized
from an improving initiative. Wraps can also be reused across related
product lines if additional product managers become aware of them.
Sometimes diffusion happens spontaneously because an innovation (e.g., a
new platform that eliminates paper-based processing) is such an obvious
improvement that it’s an easy move to replace the status quo. But usually,
even a brilliant new or improved tool, process, or product needs a nudge to
spread because spreading is rarely completely costless (e.g., people need
training to use the new platform).
When an organization sets up a shared service unit to deliver standard
reporting software and templates, it is making it easier for domain people to
apply those tools, adopting them as is or customizing them. Shared services
are great for diffusing innovations far and wide; they offer a one-to-many
relationship. For example, say the organization’s product-line unit creates a
dazzling sales dashboard that people in other units drool over. A shared
services group (the one) can spread the dazzle to any other unit that is eager
for their own drool-worthy dashboards (the many). In addition, the shared
services group can offer features like common dashboard metrics with
suggested data sources; ideal colors, visuals, and other user interface
tactics; and self-service options for dashboarding training.
Social networks, on the other hand, use many-to-many relationships to
diffuse innovations. These connections bring together red and blue
employees with common interests but different knowledge. Using social
networks, data and domain people can ask each other questions and provide
answers. Social networks can be virtual, like a Slack community, or
physical, like a data science conference or event.
The Advisory Services Connection
There is one data-domain connection—advisory services—that facilitates
both innovation and diffusion. (See the middle of figure 6.1.) It’s a super
connection and works like a consulting model: consultants learn from their
current engagement so they can spread lessons and practices to future
engagements. Everybody learns, especially the consultant. More people turn
purple, and the data democracy grows.
A lot of organizations have advisory services. They are often centers of
excellence that work with people all over the organization to solve specific
problems. Sometimes advisory services are part of the enterprise hub of a
data transformation office or in the chief data office. Centers of excellence
can also be smaller and more local, serving a specific organization area, like
research and development or a vertical line of business. Wherever they sit,
the advisory services people transfer knowledge to and from their own unit,
learning about organizational needs and spreading new knowledge about
data monetization resources throughout the organization. They spread the
wealth, so to speak.
Note that some organizations create this one structure (often as an
enterprise center of excellence) and stop there. This results in innovation
and diffusion of innovations for sure—but not enough of it. In large
organizations, a single advisory services connection will quickly become a
bottleneck and slow down the growth of a data democracy.
Connecting Structures at Microsoft
Chapter 3 described several improving initiatives that were underway at
Microsoft when the company first shifted its business model from being
product based to delivering cloud services.4 Remember how finance
shortened the time it took to get financial analyses into the hands of sales
personnel? And how enterprise sales reduced the administrative workload
of salespeople to give them more time for customer-facing activities?
The total number of improving initiatives and the amount of innovation
introduced at Microsoft during this time was staggering. One of the reasons
for the abundance of activity was the company’s thoughtful use of
organization design.5 Microsoft used all five connecting structures to
facilitate improving initiatives and elevate people’s ability to exploit their
enterprise data assets and data monetization capabilities, which fueled the
company’s business model transformation. In addition, these same
connecting structures ensured that the data monetization resources that were
made available to initiative teams were what those teams needed.
Microsoft used embedded experts and multidisciplinary teams to help
develop new work processes and new data capabilities. For example, the
data experts embedded within the enterprise sales unit were instrumental in
establishing the Microsoft Sales Experience platform and helping develop
the new enterprise sales processes that the salesforce wanted. Other units
like HR and marketing had similar embedded teams of experts and enjoyed
similar innovative outcomes.
At times, people from different parts of Microsoft came together in
multidisciplinary teams to achieve a target goal. For example, the data
science group and facilities organization—with support from people in legal
and HR—collaborated to optimize the company’s energy consumption. This
team needed to be multidisciplinary because the problem it was solving—
designing “smart” building heating and cooling solutions—was a
multidisciplinary problem.
Nadella himself leveraged a kind of multidisciplinary team to develop a
dashboard with metrics that served as leading indicators for the key pillars
of the Microsoft business. To help identify new data sources to generate the
metrics, he hosted an internal dashboard-building hackathon. Business units
across Microsoft collaborated on building a senior management dashboard;
the effort identified both the systems that held critical data and the business
owners accountable for results. The effort resulted in a new approach to
measuring the success of Microsoft’s transition to a cloud services business
model.
As innovations sprang up in one place, Microsoft leveraged shared
services to diffuse innovations that would benefit others. For example,
leadership invested in data-specific shared services groups, including one
for business intelligence that delivered templates, standard ways of
reporting, and dashboarding across the company. Other shared services
groups were responsible for diffusing other kinds of innovations: standard
sales territory geographies (owned by the data management services group),
building occupancy-related AI models (owned by the data science services
group), and emergent policies to comply with new General Data Protection
Regulation (GDPR) requirements (owned by the data governance services
group).
The business intelligence services group created social network
communities using Microsoft’s own social network platform so that users
with shared interests and concerns could engage with one another to surface
challenges and share ideas. These communities exchanged novel ways of
reporting data and deriving insights; these innovations had previously only
been used in the local business units where they originated.
Finally, Microsoft benefited from the innovation and diffusion effects of
advisory structures. For example, the CIO established a dashboard team that
consulted with Microsoft business executives to help them with their
dashboards. The dashboard team sat down with each executive and created
dashboards customized to their needs and preferences, resulting in
widespread dashboard rollout and use. As a result, these advisors got
increasingly in tune with senior management’s dashboarding needs,
continually learning new ways to meet those needs and delivering better
and more helpful support.
Data Democracy Incentives
To achieve their data democracy goals, leaders must do more than put a
smart organizational design in place. They also need to ensure that people
interact with their data or domain counterparts and learn from each other—
especially about the availability of reusable data assets and data
monetization capabilities. Employees may have little time or inclination to
interact with others, never mind engage in improving, wrapping, and selling
initiatives. Leaders must activate a smart organization design by providing
incentives to people so they will engage in connecting, innovating, and
diffusing those innovations. Otherwise, any emerging new processes or
offerings (if there are any) will not become the new normal across the
board.
To encourage people to seek out new knowledge and learn from their
available connections, organizations should consider using incentives to
induce employees to move the organization toward a data democracy. As
illustrated in figure 6.2, power, social norms, and value propositions are
three types of incentives that organizations can use to motivate employees
to become more purple so they can make full use of data monetization
resources.
Power
Leaders can use the power inherent in their formal or informal authority to
get their employees to adopt and use analytics tools, attend training, or
contribute their experiences to forums. (See the top of figure 6.2.) Leaders
use formal power when they require behavioral change and informal power
when they make it clear that they expect all employees to change their
behavior. They communicate these expectations by linking performance
evaluations to data use and by recognizing and rewarding employees’
success with data.
Nadella, for example, clearly signaled his expectations by being an
enthusiastic early adopter of the dashboard created in Microsoft’s
dashboard-building hackathon. He began actively using the dashboard to
inform his decisions, and leaders across the company soon followed suit.
Once Microsoft’s business intelligence platform was widely available,
business unit leaders became accountable for their employees’ dashboard
use. If adoption wasn’t 100 percent, leaders contacted the employees’
managers, soliciting a plan to remediate. Microsoft’s management also
established new performance metrics to push the use of connecting
structures: they adjusted employee incentives to include “collaboration
across workgroups” as one of the three core pillars on which individuals
were assessed and rewarded.
Figure 6.2
Three kinds of data democracy incentives
Social Norms
Employees are more likely to use a new dashboard, call on a customer
based on an analytics-based alert, or search the enterprise data catalog for a
new data source when their peers are doing the same thing, especially if
their peers can assuage their misgivings. (See the middle of figure 6.2.)
There is a virtuous circle effect to social norm motivation: employees
provide help and support to their peers and, in so doing, raise expectations
that others will follow suit. For example, Microsoft’s internal social
network, Yammer, served as a source of peer support for using the
company’s dashboarding tool. Yammer exchanges about the tool
encouraged employees to embrace the tool while also providing support for
those who were having trouble adapting it for their specific needs.
Microsoft leveraged social norms, in part, by making the adoption of
data-fueled decisions visible. Nadella’s hackathon-built dashboard included
a scorecard based on data supplied by many, but not all, business units
across the company. After the scorecard was launched, business units that
hadn’t initially contributed data rushed to do so; they, too, wanted a
presence on the CEO’s dashboard.
Value Proposition
Leaders tasked with driving new data monetization initiatives often find
themselves trying to win over colleagues to get them to contribute people or
funding to it. A clear value proposition—what data monetization outcomes
will mean to those involved in the initiative—can encourage people to
participate in a new improving, wrapping, or selling initiative. (See the
bottom of figure 6.2.) The value proposition becomes clear when leaders
share success stories that showcase valuable outcomes for many
stakeholders. A clear value proposition is particularly apropos for initiatives
where multidisciplinary teams are needed—each of those distinct
disciplines (or domains) might be seeking different outcomes!
Microsoft took active steps to articulate the value proposition inherent to
its transformation. Nadella frequently spoke about the role and value of data
in the company’s transformation, both to external stakeholders and within
the company. Across the company, business leaders articulated the value
proposition that was specific to their people. In the case of enterprise sales,
for example, sales leaders spoke about how the Microsoft Sales Experience
platform made work simpler and more fruitful for employees who were
engaged in fieldwork. As employees used the system and became better at
recording data about their customer interactions, they noticed that
predictions and alerts regarding their customers grew more accurate and
helpful. And, as a result, they closed more sales. The value propositions
associated with the transformation were clearly communicated, and they
triggered changes in behavior.
People need to be persuaded and encouraged to support their
organization’s desire to innovate with data. Incentives like power, social
norms, and value propositions can increase the likelihood that people will
connect with their data or domain colleagues and share expertise and data
monetization resources.
Time to Reflect
Organizations become data democracies by removing barriers for domain
experts to become aware of, access, and use data assets and capabilities and
for data experts to learn how to develop the right ones. This transformation
results in a broad upsurge in the organization’s ability to exploit data assets.
Leaders who link data and domain people enable knowledge sharing,
inspire learning, and generate innovations of all kinds. Connections that
link local efforts with centralized efforts help surface and sync up localized
innovations so they can be publicized and scaled across the organization. To
activate connections, leaders can institute “carrot and stick” techniques such
as establishing award programs that recognize employees for using data (a
carrot) and establishing accountability for data use when evaluating
employee performance (a stick). Here are the key points from this chapter
to keep in mind:
• In a data democracy, everybody in the organization can participate
in data monetization initiatives. Where in your organization are
people willing and able to be part of an improving, wrapping, or
selling initiative?
• Organizations use five data-domain connections to unleash
innovation and the diffusion of knowledge. Which of the
connections are most commonly found in your organization? How
can you develop other forms of connection?
• In a data democracy, everybody in the organization knows how to
access and use the organization’s data assets and data
monetization capabilities, if they need them. What organizational
structures are most helpful in connecting people with capabilities
in your organization?
• Even in a democracy, people need the motivation to embrace
learning from others and to change their habits. Which incentives
is your organization using to encourage more fruitful use of data
assets? What additional incentives should your organization
consider adopting?
• Innovations that are diffused as widely as possible deliver much
greater payback. Reflect on a recent experience working on any
data initiative where the team developed new practices, such as a
process to automate data quality. Did that practice spread to
other initiative teams or not? If yes, what connections or
incentives were in place? If not, what barriers were in place?
This chapter focused on describing the two key elements of a data
democracy. There is one more thing to know about data democracies: they
need direction. The next chapter is about developing a guiding vision for
your data monetization strategy.
7
Data Monetization Strategy
Having a data monetization strategy is devilishly helpful. It forces you to be clear in
your thinking. It also helps you decide what to do.
—David Lamond, Scentre Group
You’ve read about the five data monetization capabilities (data
management, data platform, data science, customer understanding, and
acceptable data use), the three kinds of data monetization initiatives
(improving, wrapping, and selling), and the five data democracy
connections that facilitate organizational innovation and diffusion. Put into
action, these frameworks will propel your organization forward, but first
you must ask, Where do we want to go?
The frameworks serve as avenues that can lead an organization toward
different ends. The same framework components can solve different
problems or achieve different objectives. You’ll need a north star to chart a
clear course. Remember the CarMax example from chapter 1? Every
CarMax employee contributes to the collective mission: either they are
trying to sell more cars or they are trying to buy more cars. What’s
important to your organization? Without a north star, it’s hard to discern the
answer to questions like the following: Which should we pursue, improving
or selling? Which capability needs the most attention, data science or
customer understanding? Which parts of our organization need to be better
connected to data assets and capabilities?
You can think of the frameworks as ingredients laid out before a skilled
chef (that’s you!). The chef can combine the ingredients into several
different delectable dishes. But to get started, she needs a vision for the
meal and an understanding of what type of palate it should satisfy. Then she
will know where to begin.
Frameworks (ingredients) in hand, you now need a data monetization
strategy. A strategy includes a goal and a plan for reaching that goal. This
chapter focuses on finding a north star—a vision—for what the organization
hopes to accomplish by monetizing data. A strategy for data monetization
sheds light on the best application of the data monetization frameworks and
what outcomes to expect from applying them. The clearer your north star,
the more focused you can be as you build capabilities, invest in initiatives,
and design your data democracy.
A data monetization strategy is a high-level plan that communicates
how an organization will improve its bottom line using its data assets.
It is a component of an organization’s data strategy.
Questions to ask yourself
Does your organization have a data monetization strategy today? If so,
who was responsible for developing it and sharing its contents?
Setting Direction with a Data Monetization
Strategy
Strategies are high-level plans that communicate what goals an organization
wants to achieve and how it will achieve those goals. A strategy focuses
resources, energy, and attention on some objectives rather than others.1 No
organization can “do it all” because no organization has limitless resources
and managerial attention. All organizations, instead, are constrained by
fixed amounts of money, people, time, energy, enthusiasm, patience, you
name it. As a result, organizations rely on clear strategies to help people
decide when to say yes or no, where to spend their time, and what results to
track.
A business strategy outlines an organization’s plan for achieving specific
business goals. A digital strategy is a plan that focuses on goals related to
digital technology and digital ways of working. It explains what an
organization will invest in to achieve those goals. A data strategy lays out
an organization’s goals and plans for managing and exploiting its data.2 It’s
a tall order to juggle and integrate so many diverse plans. It therefore helps
to think of all these strategies as nesting into each other (see figure 7.1). In
fact, the elements of a data monetization strategy—data monetization
initiatives, data monetization capabilities, and efforts to establish a data
democracy—serve as critical pieces of a general data strategy. (There are
plenty of other elements that would be found in an organization’s data
strategy, which would address issues like data security, vendor sourcing,
and talent management.)
Figure 7.1
A data monetization strategy as a component of an overall business strategy
Different readers will find themselves in different strategic situations.
Some will work for organizations with clear business strategies that their
leaders regularly articulate and reinforce. Others will struggle to find
direction from the top. Some readers will be in roles that actively contribute
to strategy, while others will feel very disconnected from their
organization’s strategy insiders. Whatever your strategic situation and role,
if you are now inspired to innovate and make money from data, you need to
add discipline to your enthusiasm. If a top-down directive is lacking, then
you can connect to local priorities. A vision will help you avoid monetizing
data in random and uncoordinated ways, which produces convenient
outcomes, not optimal ones.
This chapter will help you appreciate where your organization is heading
(so you can head in the same direction). If your organization doesn’t have a
well-marked north star, the chapter will help you identify a data
monetization direction that might work in your context. Let’s begin with the
research.
Four Data Monetization Strategy Archetypes
Just as personas stand in for users in product design, archetypes will be
used in this chapter to represent four strategies (directions) you can take to
monetize data: operational optimization, customer focus, information
business, and future ready. The name of each strategy archetype sums up its
distinct data monetization vision; it conveys why you monetize. Each
archetype reflects different bottom-line financial priorities. For example, the
operational optimization strategy prioritizes cost efficiencies, whereas the
one called customer focus prioritizes finding ways to boost sales. The
following sections describe the four archetype strategies. You can think of
them as four quick sketches of distinct data monetization strategies.
Before jumping in, let’s go over where these archetypes came from. Back
in 2018, the authors surveyed 315 data leaders about their organizations’
data monetization capabilities, initiatives, and outcomes. The research team
clustered the respondents based on their answers to three questions about
how the value they were realizing from data monetization was distributed
among three categories of value realization: cost reduction, sales increase,
or direct revenues from information offerings. Four statistically robust
clusters emerged, and the researchers followed up with some respondents to
learn more.
Figure 7.2 shows, at the top, the distribution of financial returns (cost
reduction, sales increase, or direct revenues) reported by organizations in
each strategy archetype.3 The figure also includes three indices for each
archetype. The first index, the Value Realization Index, is a composite score
that reflects how much financial value the organization is realizing (relative
to its peers). The second index, the Competitive Strength Index, was
developed from five questions asking the respondents to rate the
competitive distinctiveness of their products and information solutions. The
third index, the Data Monetization Capability Index, shows the overall
capability score for each strategy (equivalent to adding up the five
individual capability scores in the capability assessment tool in the
appendix).
Figure 7.2
Key characteristics of four data monetization strategy archetypes
Notes: a The Value Realization Index is the sum of responses to three questions about operational
efficiencies; increased product prices, sales, or loyalty; and direct revenues from selling information
solutions, using a scale of 0–5, where 0 was “we do not do this” and 1–5 ranged from “very much
below the average of our peers” to “very much above the average of our peers.” b The Competitive
Strength Index is the sum of responses to five questions measuring the competitive distinctiveness of
wrapped products and information solutions, including whether they are first to market,
groundbreaking in the marketplace, profitable, superior to those of other organizations, and highly
valued by customers, using a scale of 1–5 ranging from “strongly disagree” to “strongly agree.” c The
Data Monetization Capability Index is the sum of the scores for the five capabilities. Each individual
capability score is the average of responses to three items asking about practices that build that
capability, using a scale of 0–5, where 0 is “we do not do this practice” and 1–5 ranges from “very
poorly developed” to “very well developed.”
Operational Optimization
The operational optimization strategy starts with a vision of internal
transformation. About a quarter (24 percent) of the organizations in the
study were categorized as having an operational optimization strategy.
These organizations relied more on stripping out costs for value realization
than organizations adopting any other strategy. In fact, 90 percent of the
data monetization value they realized was in the form of cost savings,
primarily from improving initiatives. They realized some value (7 percent
of total data monetization value realized) from sales lift by making
customer-facing improvements, mainly to processes that touched and
mattered to customers. These organizations realized a small amount of
value (3 percent) from selling (probably from selling data sets to industry
data aggregators). The gains from sales lift and selling were probably
incidental to the strategy.
Organizations pursuing the operational optimization strategy had the
lowest Value Realization Index of the four archetypes. This no doubt
reflects the challenges their leaders face in turning efficiency gains into
realized value—pushing money to the bottom line. They also have the
lowest Competitive Strength Index. In the past, organizations with this
strategy would not have expected their internal processes to be
competitively distinctive. After all, many organizations use the same off-
the-shelf technology and adopt similar management approaches. But today,
you can find a lot of entrepreneurial oomph in operations. Some
organizations are packaging operating data or transaction data into
“products” or “components” that can be easily accessed and reused
internally.4 As they begin to see opportunities to offer these data products to
external users as well, the competitiveness of these products becomes more
salient to them.
Operational optimization strategy adopters had the lowest Data
Monetization Capability Index of the four archetypes. They mainly invest in
capabilities necessary for understanding and shaping operations. They
generally design their organization to connect data and domain people in
key business processes and core functional areas. An example of an
organization that adopted this strategy is Microsoft, during the company’s
business model shift to cloud services, as described in chapter 3. It focused
mainly, but not exclusively, on improvement initiatives to reshape its
operations and create new processes and work tasks. People across the
company adopted practices like dashboarding. They accessed new data
assets from cloud platforms and used all five data-domain connection
approaches to develop and diffuse innovations.
Does operational optimization sound right for your organization? Here
are some points to consider:
• Don’t discount the big bottom-line impacts that can come from
adjusting and standardizing to new, better ways of work! This
strategy might be an apt choice if your organization has an
operating model that can scale process efficiencies across
franchises, production lines, or customer touchpoints.
• If your organization is transforming like Microsoft was, the pace
and expectations of an operational optimization strategy might
align well. This strategy aligns nicely with investments in new,
more contemporary technology and systems.
• Organizations still building foundational data monetization
capabilities might find a data monetization strategy that
prioritizes improving, like operational optimization, a safe way to
get started with internal-facing initiatives.
Customer Focus
The customer focus strategy starts with a vision of using data to delight
customers. The objective is to enhance the customer experience and serve
customers more efficiently. Organizations categorized as having a customer
focus strategy realized financial returns from a combination of stripping out
costs (60 percent of total value realized from data monetization) and sales
increases (30 percent of value realized). These organizations realize some
revenues (10 percent) from charging directly for wraps as well as from
selling data sets. Thirty percent of the sample fell into this cluster. Leaders
guided by this strategy invest in a mix of wrapping and improving
initiatives because better products and better processes are usually required
to provide better service to customers.
Organizations with a customer focus strategy had the second lowest
Value Realization Index of the four archetypes. Like those pursuing
operational optimization, these organizations are no doubt challenged to
realize value by cutting budgets. It’s no less challenging for them to realize
value by repricing products. However, because they realize value from both
improving and wrapping, they achieve more overall value than
organizations in the operational optimization cluster. Customer focus
organizations also had the second lowest Competitive Strength Index.
Perhaps they concentrate more of their wrapping efforts on keeping their
products from being commoditized rather than on beating the competition.
Over time, organizations that predominantly wrap learn that their wraps will
generate larger and more lasting value if they are first to market,
groundbreaking in the marketplace, profitable, superior to those of their
competitors, and highly valued by customers.
Leaders at organizations pursuing this strategy invest in capabilities that
support the delivery of customer-facing data and analytics at high levels of
service. The average Data Monetization Capability Index for organizations
with a customer focus strategy was higher than that of organizations with an
operational optimization strategy. Customer-facing initiatives raise the
capability bar.
Organizations with the customer focus strategy use multidisciplinary
teams to connect data people with their colleagues in product management,
sales, and marketing as well as with customers. These stakeholder
relationships help organizations develop wrap offerings that customers will
love and pay for. Chapter 4 described how multidisciplinary teams were
instrumental for PepsiCo in formulating win-win customer solutions for its
retail customers (and growing its joint sphere in the process). PepsiCo
leaders also made good use of embedded experts. The company’s Demand
Accelerator put data scientists and analysts full time in marketing, sales,
and advertising so the domain experts could learn data science techniques
and dream up clever ways to leverage PepsiCo’s extensive consumer data
assets.
Does customer focus feel right to you given your organization’s current
direction? Here are some points to consider:
• Organizations with an operating model that requires offering a
superior customer experience might find this a useful strategy.
• Organizations fighting to distinguish their products in competitive
markets might find that adding useful and engaging data-fueled
features and experiences might help their offerings stand out.
• Organizations that already have digital connections with customers
(through an app, a website, or even a product) might find this
strategy to be a good fit because they can leverage those
connections to experiment and iteratively hone product features
and experiences.
• Organizations eager to transition their business customer
relationships into partnerships—as PepsiCo did in chapter 4—
might be attracted to this strategy. As they deepen their business
customer interactions and knowledge sharing, they should
become better positioned to influence customer value creation.
Information Business
The information business strategy starts with a vision of using the
organization’s data assets to solve problems for other organizations (or
consumer markets). This strategy gets people to “think like the owner of an
information business” to find innovative ways to make money from data
assets. Organizations in the information business cluster focused mainly on
realizing value from selling information solutions (65 percent) while using
wrapping initiatives to sustain the sales of those solutions (15 percent).
Sixteen percent of organizations fell into this cluster (the smallest). These
organizations usually focus on reducing the cost of delivering information
solutions (10 percent), not stripping out costs by improving processes.
Organizations in the information business cluster had the highest Value
Realization Index and the highest Competitive Strength Index of the four
archetypes. Information solutions are typically high-margin offerings, and
realizing value comes naturally to the leaders of information businesses.
(The offerings have an explicit price tag, and customers pay it!) Leaders
guided by an information business strategy develop a business model
specific to selling. They quickly learn that to sustain high margins, solutions
must be competitively distinct.
As you know from chapter 5, the technical and managerial requirements
for selling are intense. Not surprisingly, organizations with this strategy also
have the highest Data Monetization Capability Index of the four archetypes.
They invest in practices, like emergent technology or sophisticated
analytical techniques, that reduce the cost and time of processing heaps of
data. These organizations are savvy to the core; many hire only “purple
people” and then offer training and other opportunities to keep them purple.
Healthcare IQ (from chapter 5) is an excellent example of a company that
adopted an information business strategy (after it built up its data assets) to
help hospitals cope with medical supply expenditures. Many of Healthcare
IQ’s staff were former hospital health spend analysts with strong analytical
abilities. Like many organizations pursuing the information business
strategy, Healthcare IQ drew heavily on connections that foster innovation,
like embedded experts and multifunctional teams. These connections helped
the company continuously develop and adapt its competitive solutions to
cope with market dynamism.
Does an information business strategy appeal to you (irrespective of
whether your organization would be considered an information business)?
Here are some points to consider:
• Is your organization good at bringing a new product to market or at
opening altogether new markets? Organizations seeking new
sources of revenue and the exciting momentum of a product
launch might find that a strategy focused on providing new
information solutions is the right choice.
• The information business strategy can work for an organization
with an incumbent business model if it is willing to start up a
separate unit to nurture and develop selling-related initiatives and
capabilities.
• The technical requirements and organizational commitment to
deliver information solutions are no joke; selling companies like
Healthcare IQ, TRIPBAM, and the other examples featured in
chapter 5 use relentless innovation and deep market
understanding to remain viable over time. This strategy should
come with a warning label to that effect.
Future Ready
Future-ready organizations are ambidextrous: they can significantly
improve their customers’ experience relative to competitors while
relentlessly cutting costs and simplifying their own operations.5
Organizations adopting the future-ready strategy want to realize value from
data in every way possible. A future-ready data monetization strategy
inspires people enterprise-wide to be agile, reuse capabilities, seek out
ecosystem opportunities, and exploit data assets. These organizations seek
to be efficient and customer oriented at the same time, so they become
skilled at constantly making trade-offs and adjustments.
Thirty percent of the organizations fell into this archetype. They captured
financial value through cost reduction (30 percent of total data monetization
value realized), sales increase (40 percent), and direct revenues (30
percent). To do this, they pursued value realization from initiatives of all
three types—improving, wrapping, and selling. Future ready is by far the
most difficult strategy to get right because of the need to be equally great at
all three approaches. The organizations most able to pursue a future-ready
strategy are digital organizations (or digitally transformed ones) and
organizations that are incredibly mature in their data monetization practices.
Organizations in the future-ready cluster had the second highest Value
Realization Index (behind only the information business strategy) and the
second highest Competitive Strength Index of the four archetypes.
These organizations also had the second highest Data Monetization
Capability Index. It is mainly the desire to produce information solutions
that drives the need for such strong data monetization capabilities. But
excellent capabilities have a positive spillover effect in that they allow the
organization to improve and wrap more effectively and efficiently. It’s
noteworthy that companies in the future-ready cluster had the highest score
on acceptable data use capability of all four archetypes. This capability
helps these organizations exploit data assets with confidence.
Leaders with future-ready aspirations work hard to establish a data
democracy that allows employees far and wide to engage in data
monetization initiatives relevant to their work. BBVA (from chapter 2)
pursued a future-ready strategy after it added wrapping to its mix of
pursuits. The company encouraged improving, wrapping, and selling, all of
which could draw on a growing collection of enterprise data monetization
capabilities and data assets. Like Microsoft, BBVA used all five kinds of
connections between data and domain experts to facilitate widespread
innovation and the diffusion of those innovations throughout the company.
Do you think your organization is primed to pursue a future-ready
strategy? Here are some points to consider:
• Google and other digitally born companies pursue the future-ready
strategy; their employees instinctively reach for data to solve any
problem. They improve, wrap, and sell all at the same time. So,
for example, developing a proposed website feature would be
expected to lower cost to serve and drive more sales while
contributing new data to the organization’s information solutions.
Organizations with this ambidextrous culture might find this
strategy to be a good fit.
• The future-ready strategy requires data monetization initiative
owners who can pursue goals that might normally be perceived as
competing (e.g., cost reduction and sales lift). Organizations with
process, product, and information solution owners who are great
at experimentation and making trade-offs might find this strategy
appealing.
• Organizations with robust, established governance processes and
effective ways to discuss and resolve conflicting goals might find
the future-ready strategy to be a good choice.
The Four Strategy Archetypes
MIT CISR researchers often compare top- and bottom-performing
organizations to understand what drives success. For example, the data
monetization strategy study showed that top performers in all four strategy
archetypes had monetization capabilities that were just about 1.5 times as
strong as those of bottom performers. Moreover, the top performers were
realizing about twice as much value from data monetization as were low
performers. Great capabilities are vital for achieving data monetization
returns.
Your data monetization strategy should reflect the aspirations of your
organization, not your industry. None of the four archetypes is aligned to
any industry. In the research sample, for example, a fairly even number of
financial services companies fell into each of the four archetypes (19
percent, 33 percent, 16 percent, and 32 percent, respectively), and there
were financial services companies among the top and bottom performers of
each archetype. That is, financial services firms can choose to compete in
many different ways (by transforming their operations, by being customer-
centric, by selling information solutions, or all three), and financial services
firms can execute their strategies very well—or poorly.
Smart organizations do not set their strategies in stone. A data
monetization strategy is a living, evolving plan that must be adapted over
time to accommodate shifts in market dynamics, technology advancements
(think digital), an organization’s evolving capabilities, and its overarching
business strategy. Thus, the data monetization strategy ideal for an
organization today might not be ideal a year from now; it will need to be
adjusted accordingly.
Questions to ask yourself
Which strategy archetype is the right choice for your organization now?
Which one might be the right choice in five years?
Using a Value-Effort Matrix to Select Data
Monetization Initiatives
Your data monetization strategy helps establish a vision. To make progress
on your strategy, you will need to make choices among the endless list of
opportunities—and investments—in front of you. So, the first test of a
strategy might be whether it helps you prioritize data monetization
opportunities. A simple technique for doing this (an approach your
organization might already use) is to array your options on a simple two-by-
two value-effort matrix, like the one depicted in figure 7.3, by the value
they offer and the effort they require.
Figure 7.3
A value-effort matrix
Here are two ways to refine your use of a value-effort matrix using what
you have learned about data monetization. First, define the vertical
dimension of the matrix, value, in terms of your organization’s data
monetization strategy (or the archetype strategy that most resonates with
you). Instead of arraying opportunities based on, say, projected ROI or
payback period, array them on the type of value your organization’s strategy
targets. If your organization’s strategy is operational optimization, use cost
savings as the value measure. If the strategy is customer focus, use a value
measure that combines sales increase and cost savings. (Just make sure, in
the end, that the complete set of approved initiatives does not target only
sales increases or only cost savings!) If you’re pursuing an information
business strategy, use revenues (direct sales and sales increases) for your
value measure. If your chosen strategy is future ready, use bottom-line
value realization.
Depending on your strategy, high-value opportunities (easy wins and
future possibilities) might solve a costly operational workaround, they
might relieve a customer pain point, or both. Low-value opportunities
(maybes and duds) are simply not in line with business priorities even if
they are effortless to pull off. Don’t be tempted by them.
Second, adapt how you estimate effort for the horizontal dimension of the
value-effort matrix. You now know that how much effort a data
monetization opportunity will require depends significantly on the state of
the data monetization capabilities available to your initiative teams. Data
monetization opportunities that require low effort are ones for which the
organization already has the necessary data assets and capabilities in place.
Here’s an example of a low-effort initiative: the required data assets can be
accessed from a cloud platform, the team has the right data science skills,
existing algorithms can be reused or adapted, customer needs are well
understood, and an acceptable data use policy and procedures are not only
in place but automated.
Initiatives for which capabilities are needed but not available can only
reasonably be tackled in the future. But there’s a silver lining: the more
initiatives in that upper right quadrant (high value but high effort) that need
the same data asset or data monetization capability, the more value there is
in investing directly in that reusable data resource.
Another factor that affects how much effort an initiative will require is
the state of your organization’s data democracy. What connections exist that
will help facilitate the initiatives under consideration? If the way you embed
a data expert in a domain is not already established, then a lot of effort will
be required to create new HR policies, work out incentive adjustments,
relocate the expert, and get people used to sharing knowledge. On the other
hand, if it’s just a matter of filling out a form and finding a desk, less effort
will be needed. Broadly speaking, less effort will be needed in those parts
of an organization where data is already democratized.
In sum, your top priority initiative should be one that will add a lot of
strategic value—easy money—to the bottom line and for which the data
resources and organizational connections needed are already available.
They are high strategic value and low data monetization effort.
As with many other estimation tools, this one is vulnerable to bias. This
can be mitigated by ranking initiatives along each axis using an evidence-
based approach or arriving at the location of an initiative along the axis by
consensus. The value-effort matrix is an easy-to-use tool for prioritizing
data monetization opportunities, especially if it is adapted based on what
you know about data monetization.
The important thing is to pursue data monetization thoughtfully, have a
good story to describe how things will unfold, and go after outcomes that
will be valued and achievable.
Time to Reflect
A data monetization strategy indicates how an organization plans to
generate its data monetization returns. It should include an organization-
level view of goals, prioritized opportunities, targeted capabilities, and an
ideal organizational design. The process of crafting a tailored data
monetization strategy is an excellent opportunity to establish a common
language regarding data across the organization. Here are the key points
from this chapter to keep in mind:
• It’s desirable to have a data monetization strategy that can be
linked to an organization’s strategy. How widespread is the
understanding of your organization’s business strategy
throughout the organization? To what extent do your data
monetization investments reflect where your organization is
heading?
• There are four data monetization strategies that organizations
commonly pursue. Which strategy archetype is the most
consistent with the data monetization strategy being pursued by
your organization? Is your current data monetization strategy
your organization’s best choice? Looking back or looking
forward, do you see this choice changing?
• Each data monetization strategy requires different levels of
capabilities. Please take the time to assess your organization’s
data monetization capabilities using the worksheet in the
appendix if you have not already done so. Will your capabilities
support your chosen strategy? What practices does your
organization need to adopt to have the capabilities it needs to
support your desired strategy?
• Organizations need to view their capabilities and connections in
light of the data monetization strategy they are pursuing. How
hard is it for champions of new initiatives to predict when the
capabilities they need will be available? How formalized are the
connections between data and domain experts at your
organization?
You now have a vision for how you want to generate your data
monetization returns, and you probably have the germ of a data
monetization strategy. In the next chapter, you will learn that it’s time to
make it your business to monetize data.
8
Monetizing Your Data
Everybody has this idea that there is value in data, and they want to tap into it—but
there is a lack of understanding of where to begin and how to get past the hype.
—Sean Cook, Pacific Life
When you head to work tomorrow, you might encounter a new data
buzzword, a fascinating new data technique, a sexy new tech platform, or a
colleague eager to share a shocking tweet about data. There’s always
something new about data to take in, something new that you need to
evaluate, debate, choose, or figure out. (There are also weighty concerns
outside the scope of this book that merit deep thought, like ethical AI and
consumer data privacy.) You now have a starting point from which to
evaluate whether something new about data is worth investigating further.
You can put news about a competitor innovation, an industry shift, or a new
privacy regulation in context. The data world is turbulent and
entrepreneurial, always changing. Now, you should feel better prepared to
get past the hype, as Sean Cook says above.
First, a very short recap of the foundational ideas presented in this book:
• Data monetization should be like every other day-to-day activity
you perform to do your job. It shouldn’t be seen as ambiguous or
inscrutable; it is simply the act of generating financial returns
from data assets. Organizations should expect an ROI from their
expenditures in data assets, data monetization capabilities, data-
related initiatives, and data-domain connections. Data needs to be
monetized so that inflows exceed outflows.
• Data monetization comes from improving, wrapping, and selling
initiatives. It requires five enterprise capabilities and thrives in
organizations replete with connections between data and domain
experts.
• A data monetization strategy communicates specific investment
choices. A strategy helps people head in the same direction and
contribute to the organization’s top priorities.
Armed with this understanding of data monetization, you can engage
more deeply with data-related issues and see fresh opportunities in
changing circumstances. Given a chance to automate a routine process, for
example, you might first ask your manager, “How are we going to cut out
the slack this will free up?” When your competitor begins to package its
physical product with a dashboard, you will probably ask, “What will their
customers have to do to create value from that dashboard? How much value
will they realize?” If a partner approaches you with an idea to cocreate a
new information solution, you might wonder, “Do they have the
capabilities?” And when a new privacy regulation is released, you might tap
your network of “purple people,” knowing that both data and domain
perspectives will help to make an informed plan as to what needs to change.
The book concludes with suggested next steps for you to take: (1)
evaluate the current state, (2) establish a way to track your progress, and (3)
make it your business to monetize data.
Evaluate the Current State
The book’s frameworks can help you take a snapshot of the current state of
data monetization at your organization or within your unit or team. That
snapshot can serve as your baseline. Then over time, you can measure
progress against it.
Because data monetization relies on data, first consider your
organization’s key data assets. Does your organization have data assets that
are a “single source of truth” about money, customers, employees, products,
patients, legal cases, projects, or any other subject matter that is important
to your organization? Are they accurate, complete, current, standardized,
searchable, and understandable? With answers to these questions in mind,
you can begin evaluating your capabilities, initiatives, and connections.
Figure 8.1 lists the big-picture questions to ask next.
Figure 8.1
Assessing the current state of data monetization
How well does your organization produce data assets that employees
can endlessly exploit? Reusable data assets result from monetization
capabilities, which come from your organization’s data monetization
practices. You can use the capability assessment worksheet in the appendix
to evaluate your data management, data platform, data science, customer
understanding, and acceptable data use capabilities. It will help you
understand whether your practices are likely to yield capabilities (and great
data assets) at all and where your capabilities are strongest and weakest.
Next, consider another run-through with the worksheet to assess which
practices are in place across the enterprise; get a feel for which of your
organization’s capabilities are “enterprise” capabilities. Those answers will
shed light on how well your data assets have been set up for reuse.
How much are data monetization efforts paying off? Most people find
it highly informative, if not fascinating, to dig into the question of whether
your data monetization efforts are paying off. Try to quantify the outcomes
of some of your organization’s recent data monetization initiatives: What
kinds of value and how much value have been created and realized from
improving, wrapping, and selling initiatives over the last three years? Did
the initiatives achieve the sort of bottom-line returns that were expected?
Were the right people held accountable for managing the risks and
outcomes of these initiatives?
How extensively has your organization seeded or formed a data
democracy? To identify the “purple-ness” of your organization (where
purple people can be found, where red and blue people are turning purple),
evaluate your organization’s data monetization connections: Of the five
kinds of connections—embedded experts, multidisciplinary teams, shared
services, social networks, advisory services—how many are being used in
your organization? Can multifunctional teams be formed quickly, or does it
take conspiring and cajoling? Are social networks being exploited to share
knowledge about data problems and solutions? Are domain experts
motivated to interact with and learn from data experts or not?
The objective of all this questioning is to get the big picture and food for
thought. Your responses might give you confidence that your organization
is on track or they might not. Just seeing how easy or hard it is to track
down the answers to these questions might help you spot areas that need
attention!
Establish a Way to Track Progress
You can’t manage what you can’t measure. As you move ahead, inspired to
act and drive change, you will need a way to track your data monetization
progress. You might be fortunate and work in an organization with formal
data monetization monitoring or you might not. Regardless of your
organization’s measurement culture, the frameworks will offer guidance.
Capabilities, initiatives, and connections need to be measured to some
degree. If your organization already has measures to draw on, use them. If
not, you will need to formulate a way to capture information about the
components.
To give you a bit of inspiration, let’s go back to BBVA. BBVA
established a way to track its data monetization progress when it created the
BBVA D&A subsidiary. The subsidiary needed to develop a formal way to
monitor its financial health because it was expected to sell information
solutions, self-fund its operations, and engage in financial transactions with
the bank (such as paying royalties for the use of BBVA data assets). As one
of her first moves, the leader of BBVA D&A developed a framework to
classify projects based on their economic impact. She used the framework
to evaluate what kinds of value the portfolio of D&A projects would create:
some projects mainly increased sales or market size, others generated
operational efficiencies, and some created nonfinancial value (such as
contributing to BBVA capabilities).
Business units were accountable for creating and measuring the kind of
value expected from each project they sponsored. A financial expert was
hired to serve as director of finance and operations. He was responsible for
managing D&As diverse project portfolio, and he helped the business unit
leaders create appropriate measurement methodologies and validate
initiative outcomes. He ensured that the value created by the projects was
realized for both the subsidiary and the bank.
BBVA also gave D&A accountability for building enterprise capabilities
and teaching every bank employee about data science. To monitor its
progress on these goals, BBVA D&A created a dashboard that tracked the
unit’s progress in data science capability building and talent development.
The dashboard captured metrics like the number of data sets migrated from
a local database to BBVAs enterprise data platform, the number of times
existing algorithms were reused in new projects, and the number of BBVA
employees who attended data science training. It helped subsidiary leaders
identify what pockets of the bank were actively contributing to data
monetization progress; they encouraged those active pockets to continue
and tried to rouse other parts of the company from their inactivity.
Using this book’s frameworks, you can see that BBVA tracked initiatives,
capabilities, and connections. Its proprietary economic impact framework
and measurement methodologies captured whether and how initiatives were
paying off, which allowed the subsidiary leaders to ensure they invested in
a mix of project types and that the bank gained financially from data
monetization projects. Their dashboard reflected the strength of BBVAs
capabilities and data democracy.
If you, too, need to craft a way to track progress, do not feel
overwhelmed. There are two measurement principles to keep in mind. First,
measure in a way that is not overly complicated or costly. The cost of
measurement can’t be greater than the value of having the measurement in
hand. Ideally, organizations should measure just enough to sustain
organizational commitment over time. What is “just enough” will depend
on the organization’s specific needs. If the investment in a process
improvement is small, it might be enough to measure local efficiencies and
local cost reductions or slack reallocations. If the investment is large, it
might be important to surface efficiencies in downstream processes and any
related slack reduction or to measure product sales lift that can be traced to
the process improvement. Just be sure to say how you will verify that the
money reaches the bottom line.
Second, measure in a way that will be credible to the people in your
organization. Some organizations are wedded to hard evidence. They need
in-depth business cases and post-audits of every investment. Other
organizations need plausible evidence but are satisfied with anecdotes and
back-of-the-envelope extrapolations. Still others need the ongoing
production of evidence, generated by instrumentation and formalized
monitoring, for value management. Only you know what builds and
sustains commitment at your organization.
Make It Your Business to Monetize Data
This book has said at every turn that a lot of people in an organization need
to be involved in data monetization. In fact, it argues that data monetization
requires everybody. Process, product, and information solution owners must
take accountability for creating value from an organization’s data assets.
Experts need to learn from each other and share knowledge, employees
must act on insights and pursue innovations, and leaders have a duty to fund
and support the effort.
Even though data monetization takes everybody, a single person (or just a
few) can make a difference. The BBVA data monetization journey began
with four innovators who went on a sabbatical at MIT to learn about selling
information solutions. PepsiCo’s Demand Accelerator might never have
received funding had one seasoned and trusted sales leader not had the ear
of top leaders, so he could help them see the connection between analytics
and granular growth. Finally, many of the solutions and wraps that fueled
Healthcare IQ’s growth were proposed and championed by one customer
account rep who sensed a change in customer needs and helped the
company adapt its offerings and fill the need.
Now it’s your turn. Right now, your organization needs people like you to
get things started or keep things going. Look for opportunities to innovate
with data: to improve work, enhance a product, or design an information
solution. There is no better way to develop an intimate awareness of the
maturity of your data monetization resources and the challenges of pushing
money to the bottom line than to be part of an improving, wrapping, or
selling initiative team.
So start or join an initiative team. You can begin anywhere in the value-
creation process (data-insight-action), using a work challenge that you
currently face. Get some data: is there an open data set (there are over three
hundred thousand open data sets on data.gov) that could help you resolve
your challenge? Maybe you need more insight: could you use more
sophisticated number crunching? Maybe you need to standardize how you
take action: would automation help? Only through doing will you truly
learn how to monetize data.
Let’s say you decide to tackle one of these initiative ideas. Putting
together a team with all the expertise needed to launch, execute, and close
out the initiative will tell you everything you need to know about your
organization’s data-domain connections. After you get your team together,
you will need to round up the necessary data monetization capabilities. To
find capabilities, you’ll look for places where data monetization practices
are in place; the link between practices and capabilities will become crystal
clear to you. Working on your initiative, it will also become obvious why
all five capabilities are needed and why it’s better if capabilities are
enterprise capabilities.
Assuming the initiative is a smashing success, your next learning journey
will be turning the value you created into money. Your organization might
have a formal process to do this, or you may need to work with many
people to figure out how to make the organizational adjustments that will
push money to the bottom line.
When you actively engage in data monetization, you learn and you help
your organization learn. Your engagement powers the data monetization
flywheel, creating momentum that initiates a positive reinforcing cycle:
more data assets leading to more use, leading to more value, leading to
more data assets, leading to more use, and so on. Imagine this happening
across the organization as people everywhere make it their business to
monetize data.
This is why data is everybody’s business.
Appendix: The Capability Assessment Worksheet
The capability assessment worksheet in this appendix (table A.1) can be
used to assess the data monetization capabilities of your organization and
their levels (as in figure 2.3). It can also be used to calculate your
organization’s Data Monetization Capability Index (as in figure 7.2).
Table A.1
The capability assessment worksheet
Capability Typical practices
Your
score
(0–5)
Your
capability
level
(F, I, or
A)
Capability Typical practices
Your
score
(0–5)
Your
capability
level
(F, I, or
A)
Data management:
To build a data
management
capability,
organizations engage
in practices that turn
data into accurate,
integrated, and curated
data assets.
Foundational: Master
data
Practices that produce
reusable data assets
include establishing
automated data-quality
processes, identifying
data sources and flows
that describe core
business activities or key
entities like customer and
product, creating
standard definitions of
priority organizational
data fields, and
establishing metadata for
those data fields.
Intermediate:
Integrated data
Practices that allow data
to be integrated from
both internal and external
sources include mapping
and harmonizing data
sources and
standardizing, matching,
and joining data fields.
Capability Typical practices
Your
score
(0–5)
Your
capability
level
(F, I, or
A)
Advanced: Curated
data
Organizations use
taxonomy and ontology
to curate their data.
These practices involve
analyzing data and its
relationships, depicting
data and its relationships
in a way that is
accessible and
meaningful to users, and
maintaining that
depiction over time.
These practices make it
possible to augment the
organization’s data assets
with data assets from
external sources or with
data assets created as a
byproduct of the
development of AI
models.
Capability Typical practices
Your
score
(0–5)
Your
capability
level
(F, I, or
A)
Data platform: To
build a data platform
capability,
organizations engage
in practices that allow
them to draw on cloud,
open source, and
advanced database
technologies to
produce software and
hardware
configurations that
satisfy their data
processing,
management, and
delivery needs.
Foundational:
Advanced tech
The adoption of cloud-
native technologies is an
example of a data
platform practice.
Modern database
management tools
include products that
leverage leading-edge
techniques for data
compression, storage,
optimization, and
movement.
Intermediate: Internal
access
The use of APIs to offer
data and analytics
services internally is a
practice that eases access
to raw data or data assets
from any system.
Capability Typical practices
Your
score
(0–5)
Your
capability
level
(F, I, or
A)
Advanced: External
access
APIs can also be used to
make an organization’s
raw data or data assets
available to external
channels, partners, and
customers. Providing
APIs to stakeholders
outside the organization
requires adopting
practices for certifying
external users and
tracking their platform
activity.
Capability Typical practices
Your
score
(0–5)
Your
capability
level
(F, I, or
A)
Data science: To build
a data science
capability,
organizations engage
in practices that
advance their ability to
use data science
techniques and
thinking. They hire
new talent and upskill
and develop existing
employees. They
invest in tools and
methods that support
data science work so
that data science tasks
can be appropriately
managed and scaled.
Foundational:
Reporting
Practices that foster the
use of dashboards and
reporting include
standardizing data
presentation tools and
designating which data
assets will be regarded as
the “single source of
truth” for process
outcomes or business
results. They include
educating employees
about data storytelling
and evidence-based
decision-making.
Capability Typical practices
Your
score
(0–5)
Your
capability
level
(F, I, or
A)
Intermediate: Statistics
Practices that promote
the use of math and
statistics include
selecting analytics tools,
hiring people with
sophisticated
mathematical and
statistical knowledge,
and establishing data
science support units.
They include teaching
probability, statistics, and
skills that increase the
usability of analytics
tools and techniques.
Capability Typical practices
Your
score
(0–5)
Your
capability
level
(F, I, or
A)
Advanced: Machine
learning
To promote the use of
advanced analytics
techniques such as
machine learning, natural
language processing, or
image processing,
organizations engage in
feature engineering,
model training, and
model management.
They use AI explanation
practices that ensure AI
models are value
generating, compliant,
representative, and
reliable.1
Capability Typical practices
Your
score
(0–5)
Your
capability
level
(F, I, or
A)
Customer
understanding: To
build a customer
understanding
capability,
organizations connect
with customers to
collect data about them
—demographics,
sentiments, context,
usage,
Foundational:
Sensemaking
Listening to customers
and making sense of their
needs is an example of a
foundational customer
understanding practice.
Customer-facing
employees can help
organizations identify
important customer needs
by sharing ideas via
“suggestion boxes” or
crowd-sourced
innovation events. These
employees can also
participate in agile or
cross-functional teams
tasked with mapping
customer journeys or
designing new products
and processes.
Capability Typical practices
Your
score
(0–5)
Your
capability
level
(F, I, or
A)
and desires—from
which they extract
analytical insights
about core and latent
customer needs.
Intermediate:
Cocreation
Engaging customers in
the cocreation of new
products or new
processes requires
practices for identifying
the appropriate customer,
establishing the terms of
customer engagement,
and making good use of
customer time.
Advanced:
Experimentation
Common practices for
testing ideas with
customers include
hypothesis testing
(observing customer
behavior to see if it
conforms to
expectations) and the use
of A/B testing (using
randomized
experimentation with two
variants, A and B).
Capability Typical practices
Your
score
(0–5)
Your
capability
level
(F, I, or
A)
Acceptable data use:
To build an acceptable
data use capability,
organizations engage
in practices that allow
them to effectively
address regulatory and
ethical concerns
regarding data asset
use by and about
employees, partners,
and customers.
Organizations draw on
this capability to
mitigate the risk of
using data assets
inaccurately,
undesirably, or in ways
that are not
contractually or legally
allowable.
Foundational: Internal
oversight
Practices that ensure
acceptable use of data by
employees usually begin
with establishing data
ownership; training
employees about laws,
regulations, and
organizational policy;
setting up data access
approval processes; and
auditing employee data
access.
Intermediate: External
oversight
Practices that ensure the
appropriate use of data
assets by partners begin
with establishing clear
agreements about
appropriate use with
partners and end with
auditing partner use of
data assets.
Capability Typical practices
Your
score
(0–5)
Your
capability
level
(F, I, or
A)
Advanced: Automation
Practices that allow
customers to self-manage
their data begin with
establishing policies
regarding customer
control of data. These
policies are then
implemented both by
communicating the
policies to customers and
facilitating customer
control through
automation. Automating
practices also helps
organizations scale
internal and external
oversight activities.
Here’s how to use the worksheet. If your organization is large, with many
business units, you may wish to focus your assessment on the capabilities of
a particular business unit. If your focal business unit receives data-related
services from a shared services or corporate IT unit, include the practices of
that unit in your assessment since that unit’s data monetization capabilities
are made available to you.
First, score your chosen business unit on its level of adoption and use of
each of the three practices within each capability. Use a scale of 0–5 (0 =
we do not have this practice, 1 = very poorly developed, 2 = somewhat
poorly developed, 3 = of average development, 4 = somewhat well
developed, 5 = very well developed).
To assign a capability level to each asset, refer to your scores. Select the
level (foundational, intermediate, or advanced) with the highest score. If
you do not have a score of 3 or above for the foundational-level practices
for a particular capability, that capability is not yet established. If two levels
have the same score, select the higher level. For example, if the
foundational and intermediate practices are somewhat well developed, and
you gave those practices a score of 4 but some advanced practices have
been adopted but are poorly developed, scoring 1, then the level of that
capability would be intermediate. Note that the practices that build data
monetization capabilities are sequential, meaning that practice scores are
typically highest at the foundational level and lowest at the advanced level.
You can compare your scores to those provided by the 315 executives
responding to our 2018 survey,2 which can be found in the second table
(table A.2). Note that survey respondents’ capabilities were, on average, at
a foundational level. To calculate your Data Monetization Capability Index,
first average the three scores for each capability (the three levels). For
example, if for the data management capability your foundational practices
score was 4, your intermediate practices score was 4, and your advanced
practices score was 1, then the average score for this capability would be 3
[(4 + 4 + 1)/3]. Then, sum up the five capability scores. The scores for this
index range from 0 to 15.
Table A.2
Scores from 315 MIT CISR survey participants
Capability Typical practices
Average
scores
score
(0–5)
Average
capability
level
(F, I, or A)
Capability Typical practices
Average
scores
score
(0–5)
Average
capability
level
(F, I, or A)
Data
management
Foundational: Master
data
Practices that produce
reusable data assets include
establishing automated
data-quality processes,
identifying data sources
and flows that describe
core business activities or
key entities like customer
and product, creating
standard definitions of
priority organizational data
fields, and establishing
metadata for those data
fields.
3.2 Foundational
Intermediate: Integrated
data
Practices that allow data to
be integrated from both
internal and external
sources include mapping
and harmonizing data
sources and standardizing,
matching, and joining data
fields.
2.9
Capability Typical practices
Average
scores
score
(0–5)
Average
capability
level
(F, I, or A)
Advanced: Curated data
Organizations use
taxonomy and ontology to
curate their data. These
practices involve analyzing
data and its relationships,
depicting data and its
relationships in a way that
is accessible and
meaningful to users, and
maintaining that depiction
over time. These practices
make it possible to
augment the organization’s
data assets with data assets
from external sources or
with data assets created as a
byproduct of the
development of AI models.
2.5
Capability Typical practices
Average
scores
score
(0–5)
Average
capability
level
(F, I, or A)
Data platform Foundational: Advanced
tech
The adoption of cloud-
native technologies is an
example of a data platform
practice. Modern database
management tools include
products that leverage
leading-edge techniques for
data compression, storage,
optimization, and
movement.
3.1 Intermediate
Intermediate: Internal
access
The use of APIs to offer
data and analytics services
internally is a practice that
eases access to raw data or
data assets from any
system.
3.0
Capability Typical practices
Average
scores
score
(0–5)
Average
capability
level
(F, I, or A)
Advanced: External
access
APIs can also be used to
make an organization’s raw
data or data assets available
to external channels,
partners, and customers.
Providing APIs to
stakeholders outside the
organization requires
adopting practices for
certifying external users
and tracking their platform
activity.
2.3
Data science Foundational: Reporting
Practices that foster the use
of dashboards and reporting
include standardizing data
presentation tools and
designating which data
assets will be regarded as
the “single source of truth”
for process outcomes or
business results. They
include educating
employees about data
storytelling and evidence-
based decision-making.
3.6 Intermediate
Capability Typical practices
Average
scores
score
(0–5)
Average
capability
level
(F, I, or A)
Intermediate: Statistics
Practices that promote the
use of math and statistics
include selecting analytics
tools, hiring people with
sophisticated mathematical
and statistical knowledge,
and establishing data
science support units. They
include teaching
probability, statistics, and
skills that increase the
usability of analytics tools
and techniques.
3.1
Capability Typical practices
Average
scores
score
(0–5)
Average
capability
level
(F, I, or A)
Advanced: Machine
learning
To promote the use of
advanced analytics
techniques such as machine
learning, natural language
processing, or image
processing, organizations
engage in feature
engineering, model
training, and model
management. They use AI
explanation practices that
ensure AI models are value
generating, compliant,
representative, and reliable.
2.2
Capability Typical practices
Average
scores
score
(0–5)
Average
capability
level
(F, I, or A)
Customer
understanding
Foundational:
Sensemaking
Listening to customers and
making sense of their needs
is an example of a
foundational customer
understanding practice.
Customer-facing
employees can help
organizations identify
important customer needs
by sharing ideas via
“suggestion boxes” or
crowd-sourced innovation
events. These employees
can also participate in agile
or cross-functional teams
tasked with mapping
customer journeys or
designing new products and
processes.
3.1 Foundational
Capability Typical practices
Average
scores
score
(0–5)
Average
capability
level
(F, I, or A)
Intermediate: Cocreation
Engaging customers in the
cocreation of new products
or new processes requires
practices for identifying the
appropriate customer,
establishing the terms of
customer engagement, and
making good use of
customer time.
2.9
Advanced:
Experimentation
Common practices for
testing ideas with
customers include
hypothesis testing
(observing customer
behavior to see if it
conforms to expectations)
and the use of A/B testing
(using randomized
experimentation with two
variants, A and B).
2.8
Capability Typical practices
Average
scores
score
(0–5)
Average
capability
level
(F, I, or A)
Acceptable
data use
Foundational: Internal
oversight
Practices that ensure
acceptable use of data by
employees usually begin
with establishing data
ownership; training
employees about laws,
regulations, and
organizational policy;
setting up data access
approval processes; and
auditing employee data
access.
3.0 Foundational
Intermediate: External
oversight
Practices that ensure
appropriate use of data
assets by partners begin
with establishing clear
agreements about
appropriate use with
partners and end with
auditing partner use of data
assets.
2.8
Capability Typical practices
Average
scores
score
(0–5)
Average
capability
level
(F, I, or A)
Advanced: Automation
Practices that allow
customers to self-manage
their data begin with
establishing policies
regarding customer control
of data. These policies are
then implemented both by
communicating the policies
to customers and
facilitating customer
control through automation.
Automating practices also
helps organizations scale
internal and external
oversight activities.
2.3
Note: The 315 survey respondents included executives from organizations of all sizes, with 44
percent having more than US$3 billion in annual revenues in 2017 and 32 percent having revenues
under US$500 million. The majority of the executives’ organizations were for profit; 42 percent were
publicly traded, and 18 percent were nonprofit or governmental organizations. The organizations
operated worldwide, with 79 percent having some operations in North America. The organizations
competed in a range of industries; 39 percent competed in the industry categories of financial
services/banking, manufacturing, and professional services.
Source: Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and
Analytics—Summary of Survey Findings,” Working Paper No. 437, MIT Sloan Center for
Information Systems Research, April 18, 2019,
https://cisr.mit.edu/publicationMIT_CISRwp437_DataMonetizationSurveyReport_Wixom (accessed
January 17, 2023).
Acknowledgments
We are grateful to the MIT Press team for their commitment and expertise.
For their comments and their time, we are grateful to the anonymous
reviewers and to our friends and colleagues who read early versions of the
manuscript: Gregg Gullickson, Gigi Kelly, Ann Murphy, and Gary
Scholten. Collaborating with our talented graphic designer Alli Torban was
inspiring. Alli is patient, professional, intuitive, and creative. Alli’s design
process influenced our writing for the better, and her delightful graphics
will help our ideas stick.
People across MIT helped shape and strengthen our ideas. Thanks to our
MIT CISR colleagues for providing encouragement and incisive feedback:
Isobela Byerly-Chapman, Kristine Dery, Jed Diamond, Margherita DiPinto,
Chris Foglia, Nils Fonstad, Amber Franey, Dorothea Gray, Nick van der
Meulen, Cheryl Miller, Ina Sebastian, Jeanne Ross, Aman Shah, Austin Van
Groningen, Peter Weill, and Stephanie Woerner. Thank you to all our
colleagues at the MIT Sloan School of Management, the Sloan
Management Review, and the MIT Sloan research centers, with special
appreciation to Dean Schmittlein, Michael Cusumano, Elizabeth Heichler,
Abby Lundberg, and Wanda Orlikowski.
We appreciate the expertise and encouragement of the MIT Sloan
Executive Education team who guided our distillation of three decades of
data monetization research into a six-week, online data monetization
strategy course: Isabella DiMambro, Christine Gonzalez, Peter Hirst, Paul
McDonagh-Smith, Meg Regan, and the GetSmarter Team (Cara Dewar,
Andre Grobler, Pamela MacQuilkan, and John Ruzicka). These educational
designers raised questions that helped us hone our content and improve its
delivery. Also, we appreciate our executive education colleagues—and our
colleagues in the MIT Industrial Liaison Program—for introducing us to
curious global executives who actively engage with our material and help
us appreciate why and how data monetization matters to leaders of all
kinds.
This book reflects decades of research by a village of academic
collaborators. We greatly appreciate Ida Someh’s helping us understand
how organizations become data democracies and what it takes to scale AI.
Ida is a qualitative research guru who culled critical insights from MIT
CISR interviews and case studies (including at BBVA, General Electric, and
Microsoft). Thank you, Ida, for your intellectual contributions, as well as
your positive attitude and your friendship. Special thanks to Ronny Schüritz
and Killian Farrell for helping us understand how product managers use
data analytics to enhance their offerings. Ronny and Killian are gifted data
scientists who helped us develop and advance the concepts of data
wrapping and data monetization capabilities. We are grateful to Gabriele
Piccoli and Joaquin Rodriguez, who helped us examine data monetization
using their theoretical lens of digital resources. Their collaboration has
produced exciting concepts like digital data assets, actioned analytics, and
data liquidity, as well as cases such as Fidelity and TRIPBAM (the latter
along with Federico Pigni). Over the years, many other collaborators have
generously contributed their subject area expertise to assist us with specific
project needs; thank you to Anne Buff, Justin Cassey, Wynne Chin, Tom
Davenport, Tamara Dull, Dale Goodhue, Robert Gregory, Rajiv Kohli,
Dorothy Leidner, M. Lynne Markus, Anne Quaadgras, Paul Tallon, Peter
Todd, Olgerta Tona, Hugh Watson, Rick Watson, and Angela Zutavern.
It is essential for academic work to reach—and be informed by—
practitioner audiences. Thank you to the thousands of interviewees, case
study participants, and survey respondents who contributed to this research
over three decades. Special thanks to those leaders who allowed us to
feature them in our research: Linda Abraham, Magid Abraham, Scott Albin,
Elena Alfaro, Juan Murillo Arias, Sarmila Basu, Julie Batch, Tom Bayer,
Marco Bressan, Mike Brown, Peter Campbell, Tom Centlivre, Michael
Cleavinger, Reid Colson, Sean Cook, Jeff Dale, Norm Dobiesz, Jim
DuBois, Gian Fulgoni, Danny Gilligan, Enrique Hambleton, Sue Hanson,
Scott Heintzeman, Kathy Hollenhorst, Brandon Hootman, Randy Hurst,
Gregg Jankowski, Vince Jeffs, Robert Jones, Nir Kaldero, David Lamond,
Dick LeFave, Mike McClellan, Shamim Mohammad, Detlef Nauck, Sandra
Neale, Rob Phillips, Michelle Pinheiro, Vijay Raghavan, Vijay Ravi, Jeevan
Rebba, Michael Relich, Anne Marie Reynolds, Steve Reynolds, Linda
Richardson, Rajeev Ronanki, Martha Roos, Marek Rucinski, Laura Sager,
Kiki Sanchez, Mary Schapiro, Mihir Shah, Marcus Shipley, John Shomaker,
Danny Slingerland, Tim Smith, Chris Soong, Scott Stephenson, Don Stoller,
Jeff Stovall, Jeff Swearingen, Rim Tehraoui, Omid Toloui, Robert Welborn,
David Wright, Jacky Wright, Bruce Yen, and Ying Yang.
We are beholden to the global leaders who fund our work at MIT CISR
and participate in our research consortium. The current list of sponsors and
patrons is available here: https://cisr.mit.edu/content/mit-cisr-members.
Special thanks to the liaisons who provided extraordinary enthusiasm and
support for our research: Nuno Barboza, Duke Bevard, Chris Blatchly, Deb
Cassidy, Stijn Christiaens, Karen Clarke, Vittorio Cretella, Bernard
Gavgani, David Hackshall, Alexander Haneng, Craig Hopkins, Dirk van der
Horst, Naomi Jackson, Jeff Johnson, Carolyn Cameron-Kirksmith, Michelle
Mahoney, Jaime Montemayor, Mark Meyer, Robert Oh, Patrick O’Rourke,
Kal Ruberg, Tek Singh, Ivan Skerl, David Starmer, Jim Swanson, Bernardo
Tavares, Donna Vinci, Steve Whittaker, Pui Chi Wong, and Edgar van
Zoelen.
Finally, this book would not have been possible without extraordinary
contributions by the MIT CISR Data Board. Since 2015, several hundred
data and analytics leaders have piloted and perfected surveys, cleared
schedules for research interviews and virtual discussions, debated findings,
and shared struggles and wins. These lifelong learners are curious,
encouraging, and enthusiastic partners who elevate our research and help
move the field forward. We are grateful to every one of you. Thanks to
those who helped establish the board and create a spirited community:
David Abrahams, Jennifer Agnes, Laki Ahmed, Daniel Bachmann, Melanie
Bell, Aurelie Bergugnat, Michael Blumberg, Gustavo Botelho de Souza,
Gavin Burrows, Jonathan Carr, Licio Carvalho, Fiona Carver, Rafael
Cavalcanti, Harj Chand, Krishna Cheriath, Marlo Cobb, Glenn Cogar, Scott
Cooper, Tony Cossa, Glenda Crisp, José Luis Dávila, Regine Deleu, Steve
DelVecchio, Jeff DeWolf, Tej Dhawan, David Dittmann, Andrew Dobson,
Brad Fedosoff, Mavis Girlinghouse, Paul Grant, Ritesh Gupta, Sofia
Hagström, Richard Hines, Dan Holohan, Ali Kettani, Jane King, Jim
Kinzie, Joe Kleinhenz, Kate Kolich, Ram Kumar, David Lamond, Jorge
Llerena, Ling Ling Lo, Gary Lotts, Andre Luckow, Esther Málaga, Jurgen
Meerschaege, Malavika Melkote, Didem Michenet, Abhishek Mittal,
Fredrik Ohlsson, Doug Orr, Macaire Pace, Nanda Padayachee, Ajay
Padhye, Tom Pagano, Doraivelu Palanivelu, Diogo Picco, Kala
Ramaswamy, Perry Rotella, Riaan Rottier, Rob Samuel, Sai Seethala, Tom
Serven, Amy Shi-Nash, David Short, Fausto Sosa, Jim Tanner, Gilberto
Flórez Tella, Simon Thompson, Mike Trenkle, David Vaz, Kate Wei, Greg
Williams, Janine Woodside, Brett Woolley, Floyd Yager, Kelley Yohe,
Brian Zacharias, and Jenny van Zyp.
A Personal Note from Barb
This book is the result of thirty years of academic research regarding a
single question inspired by my doctoral work: How do organizations
generate value from data? Arguably, this book is my dissertation finale!
Thank you to my coauthors Cynthia Beath (known as “Boo”) and Leslie
Owens for helping me create a book I hope will help people reach their data
dreams. I appreciate your complementary contributions, your patience, and
your friendship—all of which made our collaboration a life gift.
Three influential mentors kept me on course over three decades: Hugh
Watson, Ryan Nelson, and Cynthia Beath. Hugh—you inspired me to love
data, appreciate practice, and make a difference in the field. Ryan—you
advised me through big transitions, reminded me that fun matters, and
encouraged me to reach for the stars. Boo—you saw my potential, built my
confidence, and helped me touch those stars. Hugh, Ryan, and Boo, you
mean the world to me.
Countless people influenced and inspired my work. I am grateful to my
academic and practice friends at AIS, SIG-DSA, SIM, TDWI, TUN, and
UVAs McIntire School. Special thanks to Susan Baskin, Steve Cooper,
Scott Day, Alan Dennis, Howard Dresner, Jill Dyché, Wayne Eckerson, Dan
Elron, Scott Gnau, Jane Griffin, Richard Hackathorn, Martin Holland, Cindi
Howson, Cyndy Huddleston, Claudia Imhoff, Bill Inmon, Lakshmi Iyer,
Adelaide King, Mary Lacity, Doug Laney, Evan Levy, Shawn Rogers,
Anne-Marie Smith, Catherine Szpindor, Rhian Thompson, Rich Wang,
Madeline Weiss, and Robert Winter.
Thank you to my family and personal friends for your love and support—
and your patience and understanding—as I lean into my professional
pursuits. You infuse my life with positive energy and make me a stronger
and better person.
Thank you to my husband, Chris, and my daughters, Haley and Hannah. I
am humbled by your unwavering love, encouragement, and humor during
this book journey and throughout my professional career. Your positivity
and zest for life inspire me every day. Chris, Haley, and Hannah: my heart
bursts with love for you.
A Personal Note from Cynthia (a.k.a. “Boo”)
When Jeanne W. Ross, Martin Mocker, and I sought feedback on early
drafts of our book Designed for Digital (MIT Press, 2019), the most
consistent piece of feedback we got was, “WHAT ABOUT DATA?” Our
response was, “That’s going to take a whole other book!” This is that book.
I thank Barb and Leslie for the invitation to join this team, for their patience
with me, and for the many, many laughs along the way. This has been an
incredible journey.
Some additional thanks are in order.
First and foremost, I thank my husband, Denny McCoy. Thank you,
Denny, for empathizing with my creative challenges and not trying to fix
them. Thank you for helping me face up to the blank page one day before
the last possible moment to begin. Thank you for teaching me to see that
sometimes a piece of creative work is actually “finished.” Thank you for
teaching me that it is possible to be creative and disciplined at the same
time. (I’m sorry I have not yet grasped the “creative and tidy at the same
time” lesson.) Thank you for all the hugs and cups of coffee.
Second, I thank Burt Swanson for getting me started on this fabulous
journey of research and discovery. Burt taught me to be guided in my
research by relevance to practice, and that has kept my enthusiasm for
research high throughout my career. He also showed me that research took a
lot of pure perseverance, more rewriting than seems possible, and way more
thinking than a human can tolerate. I thank him for all of that.
Third, I thank my much-missed little dog, Dolly Mama, for listening
uncritically to my ranting, for being patient with me when I worked too
long, and for always being up for a walk to breathe out the ideas that
weren’t working and breathe in some new ones.
Finally, many thanks to the universe for giving me everything I could
ever want.
A Personal Note from Leslie
Thank you to Barb and Boo for inviting me into this fun and rewarding
experience. I appreciate your humor, kindness, and can-do spirit.
Although this book is primarily about data, it’s also about people. As I
turn fifty this year, I am counting my blessings. I am lucky to have close
friends from various ages and stages of my life: childhood, college, work,
neighbors, and more. My mentors include Pauline Cochrane, Joe Coffey,
Win Lenihan, Jeff Lyons, Stephen Powers, and Rich Strle. I am so grateful
to all of you for carrying me through difficult times and helping me see and
celebrate the joys and opportunities that come my way.
Thank you to my family: Mom, Dad, Adrienne, Colleen, George, Scott,
Kelly, Erik, Graham, and Nick. My mom was a sensitive and smart
entrepreneur who gracefully balanced home and career at a time when there
were few examples out there. My dad was tenderhearted; he gave me
support and confidence. My husband, Erik, and our son, Graham: you are
the most precious people in my life. Thank you for giving me pep talks and
perspective. You set the example for hard work and goodwill that I try to
follow. I love you both with my whole heart.
Notes
Introduction
1. Miriam Daniel, “Immersive View Coming Soon to Maps—Plus More Updates,” The Keyword,
May 11, 2022, https://blog.google/products/maps/three-maps-updates-io-2022 (accessed August
30, 2022).
2. “Alphabet Q2 2022 Earnings Call Transcript,” Alphabet Investor Relations, July 26, 2022,
https://abc.xyz/investor/static/pdf/2022_Q2_Earnings_Transcript.pdf (accessed August 30, 2022).
3. Barbara H. Wixom and Gabriele Piccoli, “Build Data Liquidity to Accelerate Data Monetization,”
MIT Sloan Center for Information Systems Research, Research Briefing, vol. XXI, no. 5, May 20,
2021, https://cisr.mit.edu/publication/2021_0501_DataLiquidity_WixomPiccoli (accessed January
17, 2023).
4. Barbara H. Wixom, Thilini Ariyachandra, Michael Goul, Paul Gray, Uday Kulkarni, and Gloria
Phillips-Wren, “The Current State of Business Intelligence in Academia,” Communications of the
AIS 29, no. 1 (2011), http://aisel.aisnet.org/cais/vol29/iss1/16 (accessed January 17, 2023).
5. Mark Mosley and Michael Brackett, eds., The DAMA Guide to the Data Management Body of
Knowledge (DAMA-DMBOK Guide) (Bradley Beach, NJ: Technics Publications 2009).
Chapter 1
1. Jeanne W. Ross, Cynthia M. Beath, and R. Ryan Nelson, “Redesigning CarMax to Deliver an
Omni-Channel Customer Experience,” Working Paper No. 442, MIT Sloan Center for
Information Systems Research, June 18, 2020,
https://cisr.mit.edu/publication/MIT_CISRwp442_CarMax_RossBeathNelson (accessed January
17, 2023).
2. Ida A. Someh and Barbara H. Wixom, “Microsoft Turns to Data to Drive Business Success,”
Working Paper No. 419, MIT Sloan Center for Information Systems Research, July 28, 2017,
https://cisr.mit.edu/publication/MIT_CISRwp419_MicrosoftDataServices_SomehWixom
(accessed January 17, 2023).
3. “BBVA, an Overall Digital Experience Leader Five Years in a Row, According to ‘European
Mobile Banking Apps, Q3 2021,’” Banco Bilbao Vizcaya Argentaria, February 11, 2022,
https://www.bbva.com/en/bbva-an-overall-digital-experience-leader-five-year-in-a-row-
according-to-european-mobile-banking-apps-q3-2021/ (accessed August 30, 2022).
4. Barbara H. Wixom, “PepsiCo Unlocks Granular Growth Using a Data-Driven Understanding of
Shoppers,” Working Paper No. 439, MIT Sloan Center for Information Systems Research,
December 19, 2019, https://cisr.mit.edu/publication/MIT_CISRwp439_PepsiCoDX_Wixom
(accessed January 17, 2023).
5. Barbara H. Wixom, Killian Farrell, and Leslie Owens, “During a Crisis, Let Data Monetization
Help Your Bottom Line,” MIT Sloan Center for Information Systems Research, Research
Briefing, vol. XX, no. 4, April 16, 2020,
https://cisr.mit.edu/publication/2020_0401_DataMonPortfolio_WixomFarrellOwens (accessed
January 17, 2023).
6. Jitendra V. Singh, “Performance, Slack, and Risk Taking in Organizational Decision Making,”
The Academy of Management Journal 29, no. 3 (1986): 562–585; L. Jay Bourgeois III, “On the
Measurement of Organizational Slack,” The Academy of Management Review 6, no. 1 (1981): 29–
39.
7. Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and Analytics
—Summary of Survey Findings,” Working Paper No. 437, MIT Sloan Center for Information
Systems Research, April 18, 2019,
https://cisr.mit.edu/publication/MIT_CISRwp437_DataMonetizationSurveyReport_Wixom
(accessed January 17, 2023).
8. Steven Rosenbush and Laura Stevens, “At UPS, the Algorithm Is the Driver,” Wall Street Journal,
February 16, 2015, https://www.wsj.com/articles/at-ups-the-algorithm-is-the-driver-1424136536
(accessed August 30, 2022).
9. Clint Boulton, “Columbia Sportswear Boosts Profit with Focus on Supply Chain,” Wall Street
Journal, May 8, 2015, https://www.wsj.com/articles/columbia-sportswear-boosts-profit-with-
focus-on-supply-chain-1431121627 (accessed August 30, 2022).
10. Barbara H. Wixom, “Winning with IoT: It’s Time to Experiment,” MIT Sloan Center for
Information Systems Research, Research Briefing, vol. XVI, no. 11, November 17, 2016,
https://cisr.mit.edu/publication/2016_1101_IoT-Readiness_Wixom (accessed January 17, 2023).
11. Thomas H. Davenport and James E. Short, “The New Industrial Engineering: Information
Technology and Business Process Redesign,” Sloan Management Review (1990 Summer), 11–27;
Michael Hammer, “Reengineering Work: Don’t Automate, Obliterate!,” Harvard Business Review
(July-Aug 1990), 104–112; Michael Hammer and James Champy, Reengineering the
Corporation: A Manifesto for Business Revolution (New York: HarperBusiness, 1993); Thomas
H. Davenport, Process Innovation (Cambridge, MA: Harvard Business School Press, 1993); W.
Edwards Deming, The New Economics: For Industry, Government, Education, 3rd ed.
(Cambridge, MA: MIT Press, 2018).
12. Greg Geracie and Steven D. Eppinger, eds., The Guide to the Product Management and
Marketing Body of Knowledge: ProdBOK(R) Guide (Carson City, NV: Product Management
Educational Institute, 2013), 31.
13. Malcom Frank, Paul Roehrig, and Ben Pring, Code Halos (Hoboken, NJ: John Wiley & Sons,
2014).
14. “Our History,” Nielsen, https://sites.nielsen.com/timelines/our-history (accessed August 20,
2022); “Our Heritage of Innovation, Transformation and Growth,” IRI,
https://www.iriworldwide.com/en-us/company/history (accessed February 11, 2022).
15. Anne Buff, Barbara H. Wixom, and Paul P. Tallon, “Foundations for Data Monetization,”
Working Paper No. 402, MIT Sloan Center for Information Systems Research, August 17, 2015,
https://cisr.mit.edu/publication/MIT_CISRwp402_FoundationsForDataMonetization_BuffWixom
Tallon (accessed January 17, 2023).
16. “Business and Weather Data: Keys to Improved Decisions,” IBM,
https://www.ibm.com/products/weather-company-data-packages (accessed August 30, 2022).
Chapter 2
1. Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and Analytics
—Summary of Survey Findings,” Working Paper No. 437, MIT Sloan Center for Information
Systems Research, April 18, 2019,
https://cisr.mit.edu/publication/MIT_CISRwp437_DataMonetizationSurveyReport_Wixom
(accessed January 17, 2023).
2. Wixom, “Data Monetization.”
3. Barbara H. Wixom and Killian Farrell, “Building Data Monetization Capabilities That Pay Off,”
MIT Sloan Center for Information Systems Research, Research Briefing, vol. XIX, no. 11,
November 21, 2019,
https://cisr.mit.edu/publication/2019_1101_DataMonCapsPersist_WixomFarrell (accessed
January 17, 2023).
4. We initially noted the connections between practices and capabilities in our case research. We
confirmed these relationships in survey research; see Wixom, “Data Monetization.”
5. Barbara H. Wixom, Ida A. Someh, Angela Zutavern, and Cynthia M. Beath, “Explanation: A New
Enterprise Data Monetization Capability for AI,” Working Paper No. 443, MIT Sloan Center for
Information Systems Research, July 1, 2020,
https://cisr.mit.edu/publication/MIT_CISRwp443_SucceedingArtificialIntelligence_WixomSome
hZutavernBeath (accessed January 17, 2023).
6. Barbara H. Wixom and Gabriele Piccoli, “Build Data Liquidity to Accelerate Data Monetization,”
MIT Sloan Center for Information Systems Research, Research Briefing, vol. XXI, no. 5, May 20,
2021, https://cisr.mit.edu/publication/2021_0501_DataLiquidity_WixomPiccoli (accessed January
17, 2023).
7. Ida A. Someh, Barbara H. Wixom, and Cynthia M. Beath, “Building AI Explanation Capability
for the AI-Powered Organization,” MIT Sloan Center for Information Systems Research,
Research Briefing, vol. XXII, no. 7, July 21, 2022,
https://cisr.mit.edu/publication/2022_0701_AIX_SomehWixomBeath (accessed January 17,
2023).
8. Barbara H. Wixom and M. Lynne Markus. “To Develop Acceptable Data Use, Build Company
Norms,” MIT Sloan Center for Information Systems Research, Research Briefing, vol. XVII, no.
4, April 20, 2017, https://cisr.mit.edu/publication/2017_0401_AcceptableDataUse_WixomMarkus
(accessed January 17, 2023).
9. Barbara H. Wixom, Gabriele Piccoli, Ina Sebastian, and Cynthia M. Beath, “Anthem’s Digital
Data Sandbox,” Working Paper No. 451, MIT Sloan Center for Information Systems Research,
October 1, 2021,
https://cisr.mit.edu/publication/MIT_CISRwp451_Anthem_WixomPiccoliSebastianBeath
(accessed January 17, 2023). In 2022, Anthem Health was renamed Elevance Health (see
https://www.elevancehealth.com).
10. Elena Alfaro, Juan Murillo, Fabien Girardin, Barbara H. Wixom, and Ida A. Someh, “BBVA
Fuels Digital Transformation Progress with a Data Science Center of Excellence,” Working Paper
No. 430, MIT Sloan Center for Information Systems Research, April 27, 2018,
https://cisr.mit.edu/publication/MIT_CISRwp430_BBVADataScienceCoE_AlfaroMurilloGirardin
WixomSomeh (accessed January 17, 2023). The paper was the winner of the 2018 Best Paper
Competition from the Society for Information Management; “BBVA, an Overall Digital
Experience Leader Five Years in a Row, According to ‘European Mobile Banking Apps, Q3
2021,’” Banco Bilbao Vizcaya Argentaria, February 11, 2022, https://www.bbva.com/en/bbva-an-
overall-digital-experience-leader-five-year-in-a-row-according-to-european-mobile-banking-apps-
q3-2021 (accessed August 30, 2022).
11. Wixom and Farrell, “Building Data Monetization Capabilities That Pay Off.”
Chapter 3
1. Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and Analytics
—Summary of Survey Findings,” Working Paper No. 437, MIT Sloan Center for Information
Systems Research, April 18, 2019,
https://cisr.mit.edu/publication/MIT_CISRwp437_DataMonetizationSurveyReport_Wixom
(accessed January 17, 2023).
2. Barbara H. Wixom, Ida A. Someh, Angela Zutavern, and Cynthia M. Beath, “Explanation: A New
Enterprise Data Monetization Capability for AI,” Working Paper No. 443, MIT Sloan Center for
Information Systems Research, July 1, 2020,
https://cisr.mit.edu/publication/MIT_CISRwp443_SucceedingArtificialIntelligence_WixomSome
hZutavernBeath (accessed January 17, 2023).
3. Barbara H. Wixom, Ida A. Someh, and Robert W. Gregory, “AI Alignment: A New Management
Paradigm,” MIT Sloan Center for Information Systems Research, Research Briefing, vol. XX, no.
11, November 19, 2020, https://cisr.mit.edu/publication/2020_1101_AI-
Alignment_WixomSomehGregory (accessed January 17, 2023).
4. Barbara H. Wixom and Jeanne W. Ross, “The U.S. Securities and Exchange Commission:
Working Smarter to Protect Investors and Ensure Efficient Markets,” Working Paper No. 388,
MIT Sloan Center for Information Systems Research, November 30, 2012,
https://cisr.mit.edu/publication/MIT_CISRwp388_SEC_WixomRoss (accessed January 17, 2023).
5. Barbara H. Wixom and Anne Quaadgras, “GUESS?, Inc.: Engaging the Business Community
with the “New Look” of Business Intelligence,” MIT Sloan Center for Information Systems
Research, Research Briefing, vol. XIII, no. 8, August 15, 2013,
https://cisr.mit.edu/publication/2013_0801_GUESS_WixomQuaadgras (accessed January 17,
2023).
6. Barbara H. Wixom, “Winning with IoT: It’s Time to Experiment,” MIT Sloan Center for
Information Systems Research, Research Briefing, vol. XVI, no. 11, November 17, 2016,
https://cisr.mit.edu/publication/2016_1101_IoT-Readiness_Wixom (accessed January 17, 2023).
7. Nitan Nohria and Ranjay Gulati, “Is Slack Good or Bad for Innovation?” Academy of
Management Journal 39, no. 5 (1996): 1245–1264; Joseph L.C. Cheng and Idalene F. Kesner,
“Organizational Slack and Response to Environmental Shifts: The Impact of Resource Allocation
Patterns,” Journal of Management 23, no. 1 (1997): 1–18.
8. “Market Capitalization of Microsoft (MSFT) June 2022,” Companies Market Cap,
https://companiesmarketcap.com/microsoft/marketcap (accessed June 2022).
9. Wixom, “Data Monetization.”
10. Barbara H. Wixom and Killian Farrell, “Building Data Monetization Capabilities That Pay Off,”
MIT Sloan Center for Information Systems Research, Research Briefing, vol. XIX, no. 11,
November 21, 2019,
https://cisr.mit.edu/publication/2019_1101_DataMonCapsPersist_WixomFarrell (accessed
January 17, 2023).
Chapter 4
1. Ronny Schüritz, Killian Farrell, and Barbara H. Wixom, “Creating Competitive Products with
Analytics—Summary of Survey Findings,” Working Paper, No. 438, MIT Sloan Center for
Information Systems Research, June 28, 2019,
https://cisr.mit.edu/publication/MIT_CISRwp438_DataWrappingParticipantReport_SchuritzFarre
llWixom (accessed January 17, 2023).
2. Barbara H. Wixom and Ronny Schüritz, “Creating Customer Value Using Analytics,” MIT Sloan
Center for Information Systems Research, Research Briefing, vol. XVII, no. 11, November 16,
2017, https://cisr.mit.edu/publication/2017_1101_WrappingAtCochlear_WixomSchuritz
(accessed January 17, 2023).
3. Wixom and Schüritz, “Creating Customer Value Using Analytics.”
4. Wixom and Schüritz, “Creating Customer Value Using Analytics.”
5. Ronny Schüritz, Killian Farrell, Barbara H. Wixom, and Gerhard Satzger, “Value Co-Creation in
Data-Driven Services: Towards a Deeper Understanding of the Joint Sphere,” International
Conference for Information Systems, December 15–18, 2019; Christian Grönroos and Päivi
Voima, “Critical Service Logic: Making Sense of Value Creation and Co-Creation,” Journal of the
Academy of Marketing Science 41, no. 2 (2013): 133–150.
6. Barbara H. Wixom and Ina M. Sebastian, “Don’t Leave Value to Chance: Build Partnerships with
Customers,” MIT Sloan Center for Information Systems Research, Research Briefing, vol. XIX,
no. 12, December 19, 2019,
https://cisr.mit.edu/publication/2019_1201_PepsiCoCustomerPartnerships_WixomSebastian
(accessed January 17, 2023).
7. “PepsiCo Annual Report, 2021,” PepsiCo, https://www.pepsico.com/docs/default-source/annual-
reports/2021-annual-report.pdf (accessed August 30, 2022).
8. Barbara H. Wixom, “PepsiCo Unlocks Granular Growth Using a Data-Driven Understanding of
Shoppers,” Working Paper No. 439, MIT Sloan Center for Information Systems Research,
December 19, 2019, https://cisr.mit.edu/publication/MIT_CISRwp439_PepsiCoDX_Wixom
(accessed January 17, 2023).
9. Margaret A. Neale, and Thomas Z. Lys, Getting (More of) What You Want (London: Profile
Books, 2015).
10. Dale Goodhue and Barbara H. Wixom, “Carlson Hospitality Worldwide KAREs about Its
Customers,” in Harnessing Customer Information for Strategic Advantage: Technical Challenges
and Business Solutions, ed. W. Eckerson and H. Watson (Seattle: The Data Warehousing Institute,
2000).
11. Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and Analytics
—Summary of Survey Findings,” Working Paper No. 437, MIT Sloan Center for Information
Systems Research, April 18, 2019,
https://cisr.mit.edu/publication/MIT_CISRwp437_DataMonetizationSurveyReport_Wixom
(accessed January 17, 2023).
12. Barbara H. Wixom and Ronny Schüritz, “Making Money from Data Wrapping: Insights from
Product Managers,” MIT Sloan Center for Information Systems Research, Research Briefing, vol.
XVIII, no. 12, December 20, 2018,
https://cisr.mit.edu/publication/2018_1201_WrappingValue_WixomSchuritz (accessed January
17, 2023).
13. Barbara H. Wixom and Killian Farrell, “Building Data Monetization Capabilities That Pay Off,”
MIT Sloan Center for Information Systems Research, Research Briefing, vol. XIX, no. 11,
November 21, 2019,
https://cisr.mit.edu/publication/2019_1101_DataMonCapsPersist_WixomFarrell (accessed
January 17, 2023).
14. Wixom, “Data Monetization.”
Chapter 5
1. Anne Buff, Barbara H. Wixom, and Paul P. Tallon, “Foundations for Data Monetization,”
Working Paper No. 402, MIT Sloan Center for Information Systems Research, August 17, 2015,
https://cisr.mit.edu/publication/MIT_CISRwp402_FoundationsForDataMonetization_BuffWixom
Tallon (accessed January 17, 2023).
2. “8451: Who We Are,” 8451, https://www.8451.com/who-we-are (accessed August 30, 2022).
3. Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and Analytics
—Summary of Survey Findings,” Working Paper No. 437, MIT Sloan Center for Information
Systems Research, April 18, 2019,
https://cisr.mit.edu/publication/MIT_CISRwp437_DataMonetizationSurveyReport_Wixom
(accessed January 17, 2023).
4. Barbara H. Wixom and Jeanne W. Ross, “Profiting from the Data Deluge,” MIT Sloan Center for
Information Systems Research, Research Briefing, vol. XV, no. 12, December 17, 2015,
https://cisr.mit.edu/publication/2015_1201_DataDeluge_WixomRoss (accessed January 10,
2023).
5. Buff, Wixom, and Tallon, “Foundations for Data Monetization.”
6. “Global Data Broker Market Size, Share, Opportunities, COVID-19 Impact, and Trends by Data
Type (Consumer Data, Business Data), by End-User Industry (BFSI, Retail, Automotive,
Construction, Others), and by Geography—Forecasts from 2021 to 2026,” Knowledge Sourcing
Intelligence, June 2021, https://www.knowledge-sourcing.com/report/global-data-broker-market
(accessed August 30, 2022).
7. “About Verisk,” Verisk, https://www.verisk.com/about (accessed August 30, 2022); “Verisk Fact
Sheet,” Verisk Inc. Newsroom, https://www.verisk.com/newsroom/verisk-fact-sheet (accessed
August 30, 2022).
8. Jennifer Belissent, “The Insights Professional’s Guide to External Data Sourcing,” Forrester
Research, Inc., August 2, 2021, https://www.forrester.com/report/The-Insights-Professionals-
Guide-To-External-Data-Sourcing/RES139331 (accessible behind paywall August 30, 2022).
9. Gabriele Piccoli, Federico Pigni, Joaquin Rodriguez, and Barbara H. Wixom, “TRIPBAM:
Creating Digital Value at the Time of the COVID-19 Pandemic,” Working Paper No. 444, MIT
Sloan Center for Information Systems Research, July 30, 2020,
https://cisr.mit.edu/publication/MIT_CISRwp444_TRIPBAM_PiccoliPigniRodriguezWixom
(accessed January 10, 2023).
10. Barbara H. Wixom, Cynthia M. Beath, Ja-Nae Duane, and Ida A. Someh, “Healthcare IQ:
Sensing and Responding to Change,” Working Paper No. 458, MIT Sloan Center for Information
Systems Research, February 1, 2023,
https://cisr.mit.edu/publication/MIT_CISRwp458_HealthcareIQDataAssets_WixomBeathDuaneS
omeh (accessed February 17, 2023); Barbara H. Wixom and Cheryl Miller, “Healthcare IQ:
Competing as the ‘Switzerland’ of Health Spend Analytics,” Working Paper No. 400, MIT Sloan
Center for Information Systems Research, February 6, 2015,
https://cisr.mit.edu/publication/MIT_CISRwp400_HealthcareIQ_WixomMiller (accessed
February 17, 2023).
11. Jay B. Barney, “Looking Inside for Competitive Advantage,” The Academy of Management
Executive (1993–2005) 9, no. 4 (1995): 49–61.
12. Magid Abraham, “Data Monetization Strategies That Can Show You the Money,” MIT Sloan
Center for Information Research, MIT CISR Summer Session, June 18, 2014.
13. Barbara H. Wixom, Anne Buff, and Paul P. Tallon, “Six Sources of Value for Information
Businesses,” MIT Sloan Center for Information Systems Research, Research Briefing, vol. XV,
no. 1, January 15, 2015,
https://cisr.mit.edu/publication/2015_0101_DataMonetizationValue_WixomBuffTallon (accessed
January 10, 2023).
14. Barbara H. Wixom and Killian Farrell, “Building Data Monetization Capabilities That Pay Off,”
MIT Sloan Center for Information Systems Research, Research Briefing, vol. XIX, no. 11,
November 21, 2019,
https://cisr.mit.edu/publication/2019_1101_DataMonCapsPersist_WixomFarrell (accessed
January 17, 2023).
15. Barbara H. Wixom and M. Lynne Markus, “To Develop Acceptable Data Use, Build Company
Norms,” MIT Sloan Center for Information Systems Research, Research Briefing, vol. XVII, no.
4, April 20, 2017, https://cisr.mit.edu/publication/2017_0401_AcceptableDataUse_WixomMarkus
(accessed January 10, 2023); Dorothy Leidner, Olgerta Tona, Barbara H. Wixom, and Ida A.
Someh, “Make Dignity Core to Employee Data Use,” Sloan Management Review, September 22,
2021. Reprint #63215.
Chapter 6
1. Ida Someh, Barbara H. Wixom, Michael J Davern, and Graeme Shanks, “Configuring
Relationships Between Analytics and Business-Domain Groups for Knowledge Integration,” JAIS
Preprints (forthcoming), http://aisel.aisnet.org/jais_preprints/63 (accessed January 17, 2023).
2. Someh, Wixom, Davern, and Shanks, “Configuring Relationships.”
3. This thought experiment is inspired by events at a conference that Barbara Wixom attended years
ago hosted by TDWI (Transforming Data with Intelligence), an education and research provider.
Data leaders invited to the conference were encouraged to bring an executive business champion.
At the start of the conference, the data leaders received a red shirt and the business champions
received a blue one. At the end of the conference, everyone left with a purple shirt.
4. Ida A. Someh and Barbara H. Wixom, “Microsoft Turns to Data to Drive Business Success,”
Working Paper No. 419, MIT Sloan Center for Information Systems Research, July 28, 2017,
https://cisr.mit.edu/publication/MIT_CISRwp419_MicrosoftDataServices_SomehWixom
(accessed January 17, 2023).
5. Ida A. Someh and Barbara H. Wixom, “Data-Driven Transformation at Microsoft,” MIT Sloan
Center for Information Systems Research, Research Briefing, vol. XVII, no. 8, August 17, 2017,
https://cisr.mit.edu/publication/2017_0801_DataDrivenTransformation_SomehWixom (accessed
January 17, 2023).
Chapter 7
1. Donald C. Hambrick and James W. Frederickson, “Are You Sure You Have a Strategy?” The
Academy of Management Executive 19, no. 4 (2001): 48–59.
2. Wayne Eckerson, The Data Strategy Guidebook: What Every Executive Needs to Know (Boston,
MA: Eckerson Group, 2019).
3. Barbara H. Wixom, Killian Farrell, and Leslie Owens, “During a Crisis, Let Data Monetization
Help Your Bottom Line,” MIT Sloan Center for Information Systems Research, Research
Briefing, vol. XX, no. 4, April 16, 2020,
https://cisr.mit.edu/publication/2020_0401_DataMonPortfolio_WixomFarrellOwens (accessed
January 17, 2023).
4. Veerai Desai, Tim Fountaine, and Kayvaun Rowshankish, “A Better Way to Put Your Data to
Work,” Harvard Business Review (July-Aug 2022), 3–9.
5. Stephanie L. Woerner, Peter Weill, and Ina M. Sebastian, Future Ready: The Four Pathways to
Capturing Digital Value (Cambridge, MA: Harvard Business Review Press, 2022).
Appendix
1. Ida A. Someh, Barara H. Wixom, and Cynthia M. Beath, “Building AI Explanation Capability for
the AI-Powered Organization,” MIT Sloan Center for Information Systems, Research Briefing,
vol. XXII, no. 7, July 21, 2022,
https://cisr.mit.edu/publication/2022_0701_AIX_SomehWixomBeath (accessed January 17,
2023).
2. Barbara H. Wixom, “Data Monetization: Generating Financial Returns from Data and Analytics
—Summary of Survey Findings,” Working Paper No. 437, MIT Sloan Center for Information
Systems Research, April 18, 2019,
https://cisr.mit.edu/publication/MIT_CISRwp437_DataMonetizationSurveyReport_Wixom
(accessed January 17, 2023).
Index
Abraham, Magid, 103
A/B testing, 34, 44
Acceptable data use, 3, 29, 35–36, 59, 63, 86, 106, 108
Action improvements, 49, 52–53
Action solutions, 94, 97–98, 99–100, 101
Action wraps, 69–71, 72–73, 75, 76, 89
Advisory services connection, 115, 117, 120
Aggregators, 7, 20
AI, 33, 49, 51, 52–53, 55
Alphabet, Inc., 1
Ambiata, 34–35
Analytics tools, 33
Anthem Health, 35
Application programming interfaces (APIs), 21, 32, 86
Archetypes, data monetization strategy, 132–136
Automation, 35, 52, 97–98
Banco Bilbao Vizcaya Argentaria (BBVA). See BBVA (Banco Bilbao Vizcaya Argentaria)
Bank card data, anonymized, 40–42, 93
Bartering, 20
BBVA (Banco Bilbao Vizcaya Argentaria), 10, 40–44, 73–75, 93, 141, 151–152
BBVA Data & Analytics (D&A), 41–44, 69, 93, 151–152, 153
Bloomberg, 21
Bottom lines, 10–11, 13, 24, 81, 82
Business intelligence reporting, 49
Business Process Reengineering (BPR) movement, 17
Business strategy, 131
Business-to-business (B2B) settings, 69, 78
Business-to-consumer (B2C) settings, 69
Capabilities, data monetization, 3–4, 7–8, 25–45, 59–61, 83–87, 148
acceptable data use, 3, 29, 35–36, 59, 63, 86, 106, 108
assessment of, 36
customer understanding, 3, 29–30, 33–35, 38, 59, 61, 62–63, 87, 108
data management, 3, 27, 31–32, 59, 61–62, 86, 107
data platform, 3, 27, 32–33, 59, 62, 86, 107–108
data science, 3, 29, 31, 33, 59, 62, 86, 108
Carlson Hospitality, 82
CarMax, 9–10, 129
Chatbots, 71–72
Chief data office, 40
Cloud-based services, 57, 58
Cloud-native technology, 32, 38, 41, 86
Cocreation, 34
Collaboration, 41, 57, 79, 100, 125
Colours IQ, 99, 108
Columbia Sportswear, 17
Competitive pressure, 20, 23, 102–103
Competitive Strength Index, 133, 135, 137, 139, 141
Comscore, 103
Contractor assessments, 49, 53
Cook, Sean, 147
Copycat providers, 103
Credit scores, 96–97
Curated data, 32
Customer churn, 118
Customer focus strategy, 132, 136–138
Customer journey mapping initiatives, 9, 29–30
Customer Labs, 34
Customer retention, 20
Customer satisfaction, 22
Customer understanding, 3, 29–30, 33–35, 38, 59, 61, 62–63, 87, 108
Data access, 59, 63
Data access approval processes, 35
Data assets
accumulation of, 42–43
augmenting, 32
description of, 2
evaluation of, 149–150
external access to, 32
information solutions and, 95
new ways of working and, 10
reusing, 25, 31, 45, 63
safeguards for, 106
Data broker market, 95
Data democracies, 113–127, 145, 150
Data democracy incentives, 114, 122–126
Data-domain connections, 114–122
Data experts, 113–114
Data improvements, 49–51
Data-insight-action process, 7, 12, 22
Data management, 3, 27, 31–32, 59, 61–62, 86, 107
Data Management Association, 3
Data monetization
approaches to, 8, 16–23
capabilities for improving, 59–61
capabilities for selling, 104–106
capabilities for wrapping, 83–85
current state evaluation of, 148–151
definition of, 10
frameworks for, 3–6
percentage of in overall revenues, 12
strategy, 130–131
Data Monetization Capability Index, 130–131, 133, 136, 137, 139, 141
Data ownership, 35
Data platform, 3, 27, 32–33, 59, 62, 86, 104, 107–108
Data race, 38–40
Data savvy, 3, 114
Data science, 3, 29, 31, 33, 59, 62, 86, 108
Data solutions, 93, 95–96, 99, 101
Data sources and flows, 31–32
Data storytelling, 33
Data strategy, 131
Data wraps, 69–71, 75, 76, 89
Decontextualizing data, 2, 3–4
Definitions, standard, 31
Demand Accelerator, 79–80, 84–87, 88, 138, 153
Design thinking, 9
Diffusion connections, 119–120
Digital strategy, 131
Direction, setting, 130–132
Domain expertise, 5, 93, 104, 113–114, 115, 117
DuBois, Jim, 61
Efficiencies, 15–16, 18, 55, 80–82
Embedded experts, 115, 117–118, 121, 138
Enhancements, 18–20
Enterprise capabilities, 26, 36, 38–40, 45, 48, 61
Equipment failure predictions, 51–52
Evaluation of current state, 148–151
Evidence-based decision-making, 33, 57
Experimentation, 34
Experts/expertise, 3–6, 26, 93, 97, 104, 113–114, 138
External access, 32
External oversight, 35
eatures and experiences, 4–5
idelity Investments, 32–33
inancial analysts, 57–59, 64
orrester, award from, 10
our As, 73–75, 83
ractal maps, 103
raud reduction wraps, 82
uture-ready strategy, 132, 140–142
Gainsharing model, 101
GE, 49, 53, 55
General Data Protection Regulation (GDPR) requirements, 122
Goals, 47, 57, 65
Google, 1, 2, 57, 141
GUESS, 51, 55, 64
Handwashing, 54
Healthcare IQ, 98–100, 101, 102–103, 106–109, 139, 140, 153
HITRUST certification, 108
Hootman, Brandon, 25
Hypothesis testing, 34
AG, 34, 38
BM’s Weather Company, 21
mage processing, 33
mprove-wrap-sell framework, 21–23
mproving/improvements
capabilities for, 59–63
case study on, 42–43, 56–59, 61–63
description of, 16–18
frameworks for, 4–5
improve-wrap-sell framework and, 21–23, 24
introduction to, 47–48
ownership and, 63–64
reflection on, 64–65
types of, 49, 49–53
value creation and, 53–55
value realization and, 55–56
nformation business strategy, 132, 138–140
nformation solutions
action solutions, 94, 97–98, 99–100, 101
case study on, 98–100, 106–109
data solutions, 93, 95–96, 99, 101
nformation solutions (cont.)
description of, 20–21
insight solutions, 94, 96–97, 99, 101
selling, 4–5, 91–111
types of, 93–100
nitiatives, 4–5, 8
nnovation connections, 117–118
nsight improvements, 49, 51–52
nsight solutions, 94, 96–97, 99, 101
nsight wraps, 69–72, 75, 79, 89
ntegrated data, 31–32
nternal access, 32, 59
nternal oversight, 35
RI, 20, 27
ankowski, Gregg, 67
oint sphere, 76–78
Kroger, 92, 93
ab images, abnormalities in, 52
amond, David, 129
ocal capabilities, 26, 40
oyalty programs, 82
Machine learning, 31, 33
Madoff, Bernie, 50
Master data, 59
Measurement approaches, 53
Metadata, 31
Microsoft, 7, 10, 56–59, 61–64, 65, 120–122, 123, 125–126, 136
Microsoft Azure cloud technology, 62
Microsoft Sales Experience platform, 62, 126
MIT Center for Information Systems Research (MIT CISR), 6–7, 48, 142, 153
MIT Senseable City Lab, 40–42
Monetization, use of term, 23
Most Valuable Shopper data, 86
Multidisciplinary teams, 115, 117–118, 121, 137
Nadella, Satya, 57, 58, 59, 61, 65, 121, 123, 125–126
Natural language processing, 33
Next-best offers, 118
Nielsen, 15, 27
OM Solutions, 91–92
Ontology, 32
Open data websites, 94
Operational optimization strategy, 132, 133, 135–136
Organizational design, 5, 115, 121, 123
Owens and Minor (OM), 91, 93
Ownership
improving and, 63–64
selling and, 109–110
wrapping and, 87–88
ampers, 19
assenger demand forecasts, 52
epsiCo, 10, 78–80, 84–87, 88, 137–138, 153
hillips, Robert, 47
ilot tests, 53–54
oint-of-sale (POS) transaction data, 20, 69, 92
ower, incentives and, 123–125
ower BI, 57, 62
ractices, data monetization capabilities and, 29–36, 45
ricing strategies, 101
rivacy, 147
rocess owners, 63–64, 65
roduct enhancements, 15
roduct sales, 56
rogress, tracking, 151–153
ublic-sector initiatives, 94
urple people, 115, 117, 118, 139, 148, 150
Radisson, 82
Rebba, Jeevan, 9
Reporting, 33
Reputational risk, 88
Return on investment (ROI) from wraps, 82, 100, 147–148
Risks
improve-wrap-sell framework and, 22–23
reputational, 88
ales processes, 61–63
amuel, Rob, 113
chindler, 19
EC (US Securities and Exchange Commission), 50–51, 54
elling
capability considerations for, 104–109
case study on, 40–42
description of, 16, 20–21
improve-wrap-sell framework and, 21–23, 24
information solutions and, 91–111
ownership and, 109–110
reflection on, 110–111
value creation and, 100
value realization and, 11, 15, 101–104
ensemaking, 34, 62, 84
hah, Mihir, 1
hared services, 115, 117, 119, 122
hared value agreements, 101
lack, 15–16, 18, 55, 56, 64, 80
ocial networks, 115, 117, 119, 122
ocial norms, incentives and, 125
omeh, Ida A., 115
pecialization, 26
pend categorizers, 43–44, 69, 73–75
tatistics, 33
toller, Don, 91
torytelling training program, 58
trategy and vision, 8, 129–146, 148
witching costs, 20
Taxonomy, 32
TCR (tips, complaints, and referrals) database, 50, 54
Test and learn processes, 9
Training, 35, 51, 58
Trinity Health, 17, 52, 53–54, 64
TRIPBAM, 95–98, 100, 101, 140
Trust, 78, 79, 97–98
United Parcel Service (UPS), 17
Uplift, 76
US Securities and Exchange Commission (SEC), 50–51, 54
Value creation
description of, 11, 12
improving and, 47–48, 49, 53–55
reflection on, 23–24
risk and, 22
selling and, 100
value realization and, 15
wrapping and, 76–80
Value-effort matrix, 143–145
Value proposition, incentives and, 125–126
Value realization
description of, 11, 13–15
improve-wrap-sell framework and, 16, 24
improving and, 55–56, 59
selling and, 101–104
wrapping and, 80–83
Value Realization Index, 133, 135, 137, 139, 140
Verisk, Inc., 95
World Bank, 19
Wraps/wrapping
action wraps, 69–71, 72–73, 76, 89
capabilities for, 83–87
case study on, 43–44, 78–80, 84–87
characteristics of great, 73–75
data assets, 75
data wraps, 69–71, 75, 76, 89
description of, 16, 18–20
examples of, 67–68
improve-wrap-sell framework and, 21–23, 24
initiatives using, 87–88
insight wraps, 69–72, 75, 79, 89
joint sphere and, 76–78
measuring value from, 81–83
ownership of, 87–88
reflection on, 88–89
ROI from, 82, 100, 147–148
types of, 68–73
value creation and, 76–80
value realization and, 80–83